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\n \n \n\n \n \n \n \n \n Evolution is exponentially more powerful with frequency-dependent selection.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n bioRxiv. 2021.\n \n\n\n\n
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\n\n\n@article{EcoEvo,\r\n title={Evolution is exponentially more powerful with frequency-dependent selection},\r\n author={Kaznatcheev, Artem},\r\n journal={bioRxiv},\r\n year={2021},\r\n publisher={Cold Spring Harbor Laboratory},\r\n abstract={Valiant (2009) proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype’s fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment or static fitness landscape. Valiant’s model distinguished families of environments that are “adaptable-to” from those that are not. But this model of evolution omits the vital ecological interactions between different evolving agents – it neglects the rich biotic environment that is central to the struggle for existence.\r\n\r\nIn this article, I extend algorithmic Darwinism to include the ecological dynamics of frequency-dependent selection as a population-dependent bias to the distribution of challenges that specify an environment. Thus, extended algorithmic Darwinism suggests extended statistical queries rather than just statistical queries as the appropriate model for eco-evo dynamics. This extended algorithmic Darwinism replaces simple invasion of wild-type by a mutant-type of higher scalar fitness with an evolutionary game between wild-type and mutant-type based on their frequency-dependent fitness function. To analyze this model, I develop a game landscape view of evolution, as a generalization of the classic fitness landscape approach.\r\n\r\nI show that this model of eco-evo dynamics on game landscapes can provide an exponential speed-up over the purely evolutionary dynamics of the strict algorithmic Darwinism. In particular, I prove that the Parity environment – which is known to be not adaptable-to under strict algorithmic Darwinism – is adaptable-to by eco-evo dynamics. Thus, the ecology of frequency-dependent selection does not just increase the tempo of evolution, but fundamentally transforms its mode. This happens even if frequency-dependence is restricted to short-time scales – such short bursts of frequency-dependent selection can have a transformative effect on the ability of populations to adapt to their environments in the long-term.\r\n\r\nUnlike typical learning algorithms, the eco-evo dynamic for adapting to the Parity environment does not rely on Gaussian elimination. Instead, the dynamics proceed by simple isotropic mutations and selection in finite populations of just two types (the resident wild-type and invading mutant). The resultant process has two stages: (1) a quick stage of point-mutations that moves the population to one of exponentially many local fitness peaks; followed by (2) a slower stage where each ‘step’ follows a double-mutation by a point-mutation. This second stage allows the population to hop between local fitness peaks to reach the unique global fitness peak in polynomial time. The evolutionary game dynamics of finite populations are essential for finding a short adaptive path to the global fitness peak during the second stage of the adaptation process. This highlights the rich interface between computational learning theory, analysis of algorithms, evolutionary games, and long-term evolution.}\r\n}\r\n\r\n\n\n Valiant (2009) proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype’s fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment or static fitness landscape. Valiant’s model distinguished families of environments that are “adaptable-to” from those that are not. But this model of evolution omits the vital ecological interactions between different evolving agents – it neglects the rich biotic environment that is central to the struggle for existence. In this article, I extend algorithmic Darwinism to include the ecological dynamics of frequency-dependent selection as a population-dependent bias to the distribution of challenges that specify an environment. Thus, extended algorithmic Darwinism suggests extended statistical queries rather than just statistical queries as the appropriate model for eco-evo dynamics. This extended algorithmic Darwinism replaces simple invasion of wild-type by a mutant-type of higher scalar fitness with an evolutionary game between wild-type and mutant-type based on their frequency-dependent fitness function. To analyze this model, I develop a game landscape view of evolution, as a generalization of the classic fitness landscape approach. I show that this model of eco-evo dynamics on game landscapes can provide an exponential speed-up over the purely evolutionary dynamics of the strict algorithmic Darwinism. In particular, I prove that the Parity environment – which is known to be not adaptable-to under strict algorithmic Darwinism – is adaptable-to by eco-evo dynamics. Thus, the ecology of frequency-dependent selection does not just increase the tempo of evolution, but fundamentally transforms its mode. This happens even if frequency-dependence is restricted to short-time scales – such short bursts of frequency-dependent selection can have a transformative effect on the ability of populations to adapt to their environments in the long-term. Unlike typical learning algorithms, the eco-evo dynamic for adapting to the Parity environment does not rely on Gaussian elimination. Instead, the dynamics proceed by simple isotropic mutations and selection in finite populations of just two types (the resident wild-type and invading mutant). The resultant process has two stages: (1) a quick stage of point-mutations that moves the population to one of exponentially many local fitness peaks; followed by (2) a slower stage where each ‘step’ follows a double-mutation by a point-mutation. This second stage allows the population to hop between local fitness peaks to reach the unique global fitness peak in polynomial time. The evolutionary game dynamics of finite populations are essential for finding a short adaptive path to the global fitness peak during the second stage of the adaptation process. This highlights the rich interface between computational learning theory, analysis of algorithms, evolutionary games, and long-term evolution.\n
\n\n\n\n \n\n \n \n \n \n \n Weighted automata are compact and actively learnable.\n \n \n \n\n\n \n Kaznatcheev, A.; and Panangaden, P.\n\n\n \n\n\n\n Information Processing Letters. 2021.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2021weighted,\r\n title={Weighted automata are compact and actively learnable},\r\n author={Kaznatcheev, Artem and Panangaden, Prakash},\r\n journal={Information Processing Letters},\r\n year={2021},\r\n abstract={We show that weighted automata over the field of two elements can be exponentially more compact than non-deterministic finite state automata. To show this, we combine ideas from automata theory and communication complexity. However, weighted automata are also efficiently learnable in Angluin's minimal adequate teacher model in a number of queries that is polynomial in the size of the minimal weighted automaton. We include an algorithm for learning WAs over any field based on a linear algebraic generalization of the Angluin-Schapire algorithm. Together, this produces a surprising result: weighted automata over fields are structured enough that even though they can be very compact, they are still efficiently learnable.}\r\n}\r\n\n\n We show that weighted automata over the field of two elements can be exponentially more compact than non-deterministic finite state automata. To show this, we combine ideas from automata theory and communication complexity. However, weighted automata are also efficiently learnable in Angluin's minimal adequate teacher model in a number of queries that is polynomial in the size of the minimal weighted automaton. We include an algorithm for learning WAs over any field based on a linear algebraic generalization of the Angluin-Schapire algorithm. Together, this produces a surprising result: weighted automata over fields are structured enough that even though they can be very compact, they are still efficiently learnable.\n
\n\n\n\n \n\n \n \n \n \n \n The contribution of evolutionary game theory to understanding and treating cancer.\n \n \n \n\n\n \n Wölfl, B.; te Rietmole, H.; Salvioli, M.; Kaznatcheev, A.; Thuijsman, F.; Brown, J. S; Burgering, B.; and Stankova, K.\n\n\n \n\n\n\n medRxiv. 2021.\n \n\n\n\n
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\n\n\n@article{wolfl2021contribution,\r\n title={The contribution of evolutionary game theory to understanding and treating cancer},\r\n author={W{\\"o}lfl, Benjamin and te Rietmole, Hedy and Salvioli, Monica and Kaznatcheev, Artem and Thuijsman, Frank and Brown, Joel S and Burgering, Boudewijn and Stankova, Katerina},\r\n journal={medRxiv},\r\n year={2021},\r\n publisher={Dynamic Games and Applications},\r\n abstract={Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with an evolutionary game theory approach has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary game and needs to be viewed as such.}\r\n}\r\n\n\n Evolutionary game theory mathematically conceptualizes and analyzes biological interactions where one’s fitness not only depends on one’s own traits, but also on the traits of others. Typically, the individuals are not overtly rational and do not select, but rather inherit their traits. Cancer can be framed as such an evolutionary game, as it is composed of cells of heterogeneous types undergoing frequency-dependent selection. In this article, we first summarize existing works where evolutionary game theory has been employed in modeling cancer and improving its treatment. Some of these game-theoretic models suggest how one could anticipate and steer cancer’s eco-evolutionary dynamics into states more desirable for the patient via evolutionary therapies. Such therapies offer great promise for increasing patient survival and decreasing drug toxicity, as demonstrated by some recent studies and clinical trials. We discuss clinical relevance of the existing game-theoretic models of cancer and its treatment, and opportunities for future applications. Moreover, we discuss the developments in cancer biology that are needed to better utilize the full potential of game-theoretic models. Ultimately, we demonstrate that viewing tumors with an evolutionary game theory approach has medically useful implications that can inform and create a lockstep between empirical findings and mathematical modeling. We suggest that cancer progression is an evolutionary game and needs to be viewed as such.\n
\n\n\n\n \n\n \n \n \n \n \n Measuring competitive exclusion in non-small cell lung cancer.\n \n \n \n\n\n \n Farrokhian, N.; Maltas, J.; Ellsworth, P.; Durmaz, A.; Dinh, M.; Hitomi, M.; McClure, E.; Marusyk, A.; Kaznatcheev, A.; and Scott, J. G\n\n\n \n\n\n\n bioRxiv. 2021.\n \n\n\n\n
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\n\n\n@article{gameAssayCompetitiveExclusion,\r\n title={Measuring competitive exclusion in non-small cell lung cancer},\r\n author={Farrokhian, Nathan and Maltas, Jeff and Ellsworth, Patrick and Durmaz, Arda and Dinh, Mina and Hitomi, Masahiro and McClure, Erin and Marusyk, Andriy and Kaznatcheev, Artem and Scott, Jacob G},\r\n journal={bioRxiv},\r\n year={2021},\r\n publisher={Cold Spring Harbor Laboratory},\r\n abstract={Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies attempt to delay resistance via judicious treatments that maintain a significant treatable subpopulation. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. In this study we experimentally measure the frequency-dependent interactions between a gefitinib resistant non-small cell lung cancer (NSCLC) population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. In addition, we show that frequency-dependent growth rate changes may ultimately result in a safe harbor for resistant populations to safely accumulate, even those with significant cost of resistance. Using frequency-dependent growth rate data we then show that gefitinib treatment results in competitive exclusion of the ancestor, while absence of treatment results in a likely, but not guaranteed exclusion of the resistant strain. Finally, using our empirically derived growth rates to constrain simulations, we demonstrate that incorporating ecological growth effects can dramatically change the predicted time to sensitive strain extinction. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Taken together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and the clinic.}\r\n}\r\n\n\n Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies attempt to delay resistance via judicious treatments that maintain a significant treatable subpopulation. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. In this study we experimentally measure the frequency-dependent interactions between a gefitinib resistant non-small cell lung cancer (NSCLC) population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. In addition, we show that frequency-dependent growth rate changes may ultimately result in a safe harbor for resistant populations to safely accumulate, even those with significant cost of resistance. Using frequency-dependent growth rate data we then show that gefitinib treatment results in competitive exclusion of the ancestor, while absence of treatment results in a likely, but not guaranteed exclusion of the resistant strain. Finally, using our empirically derived growth rates to constrain simulations, we demonstrate that incorporating ecological growth effects can dramatically change the predicted time to sensitive strain extinction. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Taken together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and the clinic.\n
\n\n\n\n \n\n \n \n \n \n \n Measuring as a new mode of inquiry that bridges evolutionary game theory and cancer biology.\n \n \n \n\n\n \n Kaznatcheev, A.; and Lin, C.\n\n\n \n\n\n\n . 2021.\n \n\n\n\n
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\n\n\n@article{gameAssayPhilSci,\r\n title={Measuring as a new mode of inquiry that bridges evolutionary game theory and cancer biology},\r\n author={Kaznatcheev, Artem and Lin, Chia-Hua},\r\n year={2021},\r\n abstract={We show that as game theory was transferred from mathematical oncology to experimental cancer biology, a new mode of inquiry was created. Modelling was replaced by measuring. The game measured by a game assay can serve as a bridge that allows knowledge to flow backwards from target (cancer research) to source (game theory). Our finding suggests that the conformist and creative (Houkes and Zwart, 2019) types of transfer need to be augmented. We conclude by introducing the expansive and transformative types to get a four-tier typology of knowledge transfer.}\r\n}\r\n\n\n We show that as game theory was transferred from mathematical oncology to experimental cancer biology, a new mode of inquiry was created. Modelling was replaced by measuring. The game measured by a game assay can serve as a bridge that allows knowledge to flow backwards from target (cancer research) to source (game theory). Our finding suggests that the conformist and creative (Houkes and Zwart, 2019) types of transfer need to be augmented. We conclude by introducing the expansive and transformative types to get a four-tier typology of knowledge transfer.\n
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\n \n \n\n \n \n \n \n \n Representing fitness landscapes by valued constraints to understand the complexity of local search.\n \n \n \n\n\n \n Kaznatcheev, A.; Cohen, D.; and Jeavons, P.\n\n\n \n\n\n\n Journal of Artificial Intelligence Research, 69: 1077–1102. 2020.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2020representing,\r\n title={Representing fitness landscapes by valued constraints to understand the complexity of local search},\r\n author={Kaznatcheev, Artem and Cohen, David and Jeavons, Peter},\r\n journal={Journal of Artificial Intelligence Research},\r\n volume={69},\r\n pages={1077--1102},\r\n year={2020},\r\n abstract={Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fitness landscapes is poorly understood. Here we consider how fitness landscapes can be represented using valued constraints, and investigate what the structure of such representations reveals about the complexity of local search.}\r\n}\r\n\r\n\n\n Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fitness landscapes is poorly understood. Here we consider how fitness landscapes can be represented using valued constraints, and investigate what the structure of such representations reveals about the complexity of local search.\n
\n\n\n\n \n\n \n \n \n \n \n Steepest ascent can be exponential in bounded treewidth problems.\n \n \n \n\n\n \n Cohen, D. A; Cooper, M. C; Kaznatcheev, A.; and Wallace, M.\n\n\n \n\n\n\n Operations Research Letters, 48(3): 217–224. 2020.\n \n\n\n\n
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\n\n\n@article{cohen2020steepest,\r\n title={Steepest ascent can be exponential in bounded treewidth problems},\r\n author={Cohen, David A and Cooper, Martin C and Kaznatcheev, Artem and Wallace, Mark},\r\n journal={Operations Research Letters},\r\n volume={48},\r\n number={3},\r\n pages={217--224},\r\n year={2020},\r\n publisher={North-Holland},\r\n abstract={We investigate the complexity of local search based on steepest ascent. We show that even when all variables have domains of size two and the underlying constraint graph of variable interactions has bounded treewidth (in our construction, treewidth 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum. This is an improvement on prior recursive constructions of long steepest ascents, which we prove to need constraint graphs of unbounded treewidth.}\r\n}\r\n\r\n\n\n We investigate the complexity of local search based on steepest ascent. We show that even when all variables have domains of size two and the underlying constraint graph of variable interactions has bounded treewidth (in our construction, treewidth 7), there are fitness landscapes for which an exponential number of steps may be required to reach a local optimum. This is an improvement on prior recursive constructions of long steepest ascents, which we prove to need constraint graphs of unbounded treewidth.\n
\n\n\n\n \n\n \n \n \n \n \n Algorithmic biology of evolution and ecology.\n \n \n \n\n\n \n Kaznatcheev, A\n\n\n \n\n\n\n Ph.D. Thesis, University of Oxford, 2020.\n \n\n\n\n
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\n\n\n@phdthesis{kaznatcheev2020algorithmic,\r\n title={Algorithmic biology of evolution and ecology},\r\n author={Kaznatcheev, A},\r\n year={2020},\r\n school={University of Oxford},\r\n abstract={Any process can be seen as an algorithm; its power and its limits can then be analysed with the techniques of theoretical computer science. To analyse algorithms, we divide the world in two: the problem space that shapes what might happen and the dynamics of what does happen. If we fix an idealised framework for one of the two, then we can obtain powerful general results by abstracting over the other. This “algorithmic lens” can be used to view both artificial and natural processes, including the natural processes of biological evolution.\r\n\r\n In Part I, I idealize the space of evolution as a fitness landscape so that I can abstract over the possible evolutionary dynamics. I show that fitness landscapes can be represented by gene-interaction networks that encode the structure of epistasis.\r\n\r\n For some landscapes, the epistatic structure produces a computational constraint that prevents evolution from finding even a local fitness optimum—thus contradicting the traditional assumption that local fitness peaks can always be reached quickly by natural selection. I introduce a distinction between easy landscapes, where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding any such local optimum requires an infeasible amount of time. Hard examples exist where strong-selection weak-mutation dynamics cannot find a local peak in polynomial time, even when it is known to be unique. More generally, I show that hard fitness landscapes exist where no evolutionary dynamics—even ones that do not follow adaptive paths—can find a local fitness optimum in polynomial time. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but must follow a power-law, similar to the one found by long-term evolution experiments, associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Finally, by looking closer at the fine structure of epistasis, I also extend the class of provably easy landscapes to include all those with tree-structured gene-interaction networks.\r\n\r\n In Part II, I idealize the dynamics of evolution as replicator dynamics so that I can abstract over the space of ecologies (interactions between organisms). This requires replacing the fitness-as-scalar concept used in fitness landscapes by a fitness-as-function concept derived from evolutionary game theory. Since they have not been adequately defined or interpreted in the context of microscopic biology, I provide two interpretations of the central objects of game theory: one that leads to what I call “reductive games” and the other to “effective games”. These interpretations are based on the difference between views of fitness as a property of tokens versus fitness as a summary statistic of types. Reductive games are typical of theoretical work like agent-based models. Effective games correspond more closely to experimental work and allow for empirical abstraction over poorly characterized interaction mechanisms like spatial structure.\r\n\r\n This empirical abstraction allows me to analyse the in vitro evolution of resistance to cancer therapy. I develop a game assay to directly measure effective evolutionary games in co-cultures of non-small cell lung cancer cells that are sensitive vs resistant to the targeted drug Alectinib. I show that the games are not only quantitatively different between different environments, but that the presence of the drug or the absence of cancer-associated fibroblasts qualitatively switches the type of game being played by the in vitro population. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game.\r\n\r\n Thus through the whole thesis, I demonstrate how the algorithmic lens and abstraction can help us derive new ways of seeing and understanding both evolution and ecology.}\r\n}\r\n\r\n\n\n Any process can be seen as an algorithm; its power and its limits can then be analysed with the techniques of theoretical computer science. To analyse algorithms, we divide the world in two: the problem space that shapes what might happen and the dynamics of what does happen. If we fix an idealised framework for one of the two, then we can obtain powerful general results by abstracting over the other. This “algorithmic lens” can be used to view both artificial and natural processes, including the natural processes of biological evolution. In Part I, I idealize the space of evolution as a fitness landscape so that I can abstract over the possible evolutionary dynamics. I show that fitness landscapes can be represented by gene-interaction networks that encode the structure of epistasis. For some landscapes, the epistatic structure produces a computational constraint that prevents evolution from finding even a local fitness optimum—thus contradicting the traditional assumption that local fitness peaks can always be reached quickly by natural selection. I introduce a distinction between easy landscapes, where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding any such local optimum requires an infeasible amount of time. Hard examples exist where strong-selection weak-mutation dynamics cannot find a local peak in polynomial time, even when it is known to be unique. More generally, I show that hard fitness landscapes exist where no evolutionary dynamics—even ones that do not follow adaptive paths—can find a local fitness optimum in polynomial time. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but must follow a power-law, similar to the one found by long-term evolution experiments, associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Finally, by looking closer at the fine structure of epistasis, I also extend the class of provably easy landscapes to include all those with tree-structured gene-interaction networks. In Part II, I idealize the dynamics of evolution as replicator dynamics so that I can abstract over the space of ecologies (interactions between organisms). This requires replacing the fitness-as-scalar concept used in fitness landscapes by a fitness-as-function concept derived from evolutionary game theory. Since they have not been adequately defined or interpreted in the context of microscopic biology, I provide two interpretations of the central objects of game theory: one that leads to what I call “reductive games” and the other to “effective games”. These interpretations are based on the difference between views of fitness as a property of tokens versus fitness as a summary statistic of types. Reductive games are typical of theoretical work like agent-based models. Effective games correspond more closely to experimental work and allow for empirical abstraction over poorly characterized interaction mechanisms like spatial structure. This empirical abstraction allows me to analyse the in vitro evolution of resistance to cancer therapy. I develop a game assay to directly measure effective evolutionary games in co-cultures of non-small cell lung cancer cells that are sensitive vs resistant to the targeted drug Alectinib. I show that the games are not only quantitatively different between different environments, but that the presence of the drug or the absence of cancer-associated fibroblasts qualitatively switches the type of game being played by the in vitro population. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Thus through the whole thesis, I demonstrate how the algorithmic lens and abstraction can help us derive new ways of seeing and understanding both evolution and ecology.\n
\n\n\n\n \n\n \n \n \n \n \n IsoMaTrix: a framework to visualize the isoclines of matrix games and quantify uncertainty in structured populations.\n \n \n \n\n\n \n West, J.; Ma, Y.; Kaznatcheev, A.; and Anderson, A.\n\n\n \n\n\n\n Bioinformatics,btaa1025. 2020.\n \n\n\n\n
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\n\n\n@article{west2020isomatrix,\r\n title={IsoMaTrix: a framework to visualize the isoclines of matrix games and quantify uncertainty in structured populations},\r\n author={West, Jeffrey and Ma, Yongqian and Kaznatcheev, Artem and Anderson, Alexander},\r\n journal={Bioinformatics},\r\n pages={btaa1025},\r\n year={2020},\r\n abstract={Evolutionary game theory describes frequency-dependent selection for fixed, heritable strategies in a population of competing individuals using a payoff matrix, typically described using well-mixed assumptions (replicator dynamics). IsoMaTrix is an open-source package which computes the isoclines (lines of zero growth) of matrix games, and facilitates direct comparison of well-mixed dynamics to structured populations in two or three dimensions. IsoMaTrix is coupled with a Hybrid Automata Library module to simulate structured matrix games on-lattice. IsoMaTrix can also compute fixed points, phase flow, trajectories, velocities (and subvelocities), delineated "region plots" of positive/negative strategy velocity, and uncertainty quantification for stochastic effects in structured matrix games. We describe a result obtained via IsoMaTrix9s spatial games functionality, which shows that the timing of competitive release in a cancer model (under continuous treatment) critically depends on the initial spatial configuration of the tumor.}\r\n}\r\n\n\n Evolutionary game theory describes frequency-dependent selection for fixed, heritable strategies in a population of competing individuals using a payoff matrix, typically described using well-mixed assumptions (replicator dynamics). IsoMaTrix is an open-source package which computes the isoclines (lines of zero growth) of matrix games, and facilitates direct comparison of well-mixed dynamics to structured populations in two or three dimensions. IsoMaTrix is coupled with a Hybrid Automata Library module to simulate structured matrix games on-lattice. IsoMaTrix can also compute fixed points, phase flow, trajectories, velocities (and subvelocities), delineated \"region plots\" of positive/negative strategy velocity, and uncertainty quantification for stochastic effects in structured matrix games. We describe a result obtained via IsoMaTrix9s spatial games functionality, which shows that the timing of competitive release in a cancer model (under continuous treatment) critically depends on the initial spatial configuration of the tumor.\n
\n\n\n\n \n\n \n \n \n \n \n Understanding the Evolutionary Games in NSCLC Microenvironment.\n \n \n \n\n\n \n Bhattacharya, R.; Vander Velde, R.; Marusyk, V.; Desai, B.; Kaznatcheev, A.; Marusyk, A.; and Basanta, D.\n\n\n \n\n\n\n bioRxiv. 2020.\n \n\n\n\n
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\n\n\n@article{gameAssay3Strat,\r\n title={Understanding the Evolutionary Games in NSCLC Microenvironment},\r\n author={Bhattacharya, Ranjini and Vander Velde, Robert and Marusyk, Viktoriya and Desai, Bina and Kaznatcheev, Artem and Marusyk, Andriy and Basanta, David},\r\n journal={bioRxiv},\r\n year={2020},\r\n publisher={Cold Spring Harbor Laboratory},\r\n abstract={While initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking.\r\n\r\n Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naïve tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naïve cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.}\r\n}\r\n\n\n While initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking. Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naïve tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naïve cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.\n
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\n \n \n\n \n \n \n \n \n Computational complexity as an ultimate constraint on evolution.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n Genetics, 212(1): 245–265. 2019.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2019computational,\r\n title={Computational complexity as an ultimate constraint on evolution},\r\n author={Kaznatcheev, Artem},\r\n journal={Genetics},\r\n volume={212},\r\n number={1},\r\n pages={245--265},\r\n year={2019},\r\n publisher={Oxford University Press},\r\n abstract={Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima—thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semismooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics—even ones that do not follow adaptive paths—can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.}\r\n}\r\n\r\n\n\n Experiments show that evolutionary fitness landscapes can have a rich combinatorial structure due to epistasis. For some landscapes, this structure can produce a computational constraint that prevents evolution from finding local fitness optima—thus overturning the traditional assumption that local fitness peaks can always be reached quickly if no other evolutionary forces challenge natural selection. Here, I introduce a distinction between easy landscapes of traditional theory where local fitness peaks can be found in a moderate number of steps, and hard landscapes where finding local optima requires an infeasible amount of time. Hard examples exist even among landscapes with no reciprocal sign epistasis; on these semismooth fitness landscapes, strong selection weak mutation dynamics cannot find the unique peak in polynomial time. More generally, on hard rugged fitness landscapes that include reciprocal sign epistasis, no evolutionary dynamics—even ones that do not follow adaptive paths—can find a local fitness optimum quickly. Moreover, on hard landscapes, the fitness advantage of nearby mutants cannot drop off exponentially fast but has to follow a power-law that long-term evolution experiments have associated with unbounded growth in fitness. Thus, the constraint of computational complexity enables open-ended evolution on finite landscapes. Knowing this constraint allows us to use the tools of theoretical computer science and combinatorial optimization to characterize the fitness landscapes that we expect to see in nature. I present candidates for hard landscapes at scales from single genes, to microbes, to complex organisms with costly learning (Baldwin effect) or maintained cooperation (Hankshaw effect). Just how ubiquitous hard landscapes (and the corresponding ultimate constraint on evolution) are in nature becomes an open empirical question.\n
\n\n\n\n \n\n \n \n \n \n \n The 2019 Mathematical Oncology Roadmap.\n \n \n \n\n\n \n Rockne, R. C; Hawkins-Daarud, A.; Swanson, K. R; Sluka, J. P; Glazier, J. A; Macklin, P.; Hormuth, D. A; Jarrett, A. M; Lima, E. A B F; Oden, J T.; Biros, G.; Yankeelov, T. E; Curtius, K.; Bakir, I. A.; Wodarz, D.; Komarova, N.; Aparicio, L.; Bordyuh, M.; Rabadan, R.; Finley, S. D; Enderling, H.; Caudell, J.; Moros, E. G; Anderson, A. R A; Gatenby, R. A; Kaznatcheev, A.; Jeavons, P.; Krishnan, N.; Pelesko, J.; Wadhwa, R. R; Yoon, N.; Nichol, D.; Marusyk, A.; Hinczewski, M.; and Scott, J. G\n\n\n \n\n\n\n Physical Biology, 16(4): 041005. 2019.\n \n\n\n\n
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\n\n\n@article{mathoncoRoadmap,\r\n title={The 2019 Mathematical Oncology Roadmap},\r\n author={Russell C Rockne and Andrea Hawkins-Daarud and Kristin R Swanson and James P Sluka and James A Glazier and Paul Macklin and David A Hormuth and Angela M Jarrett and Ernesto A B F Lima and J Tinsley Oden and George Biros and Thomas E Yankeelov and Kit Curtius and Ibrahim Al Bakir and Dominik Wodarz and Natalia Komarova and Luis Aparicio and Mykola Bordyuh and Raul Rabadan and Stacey D Finley and Heiko Enderling and Jimmy Caudell and Eduardo G Moros and Alexander R A Anderson and Robert A Gatenby and Artem Kaznatcheev and Peter Jeavons and Nikhil Krishnan and Julia Pelesko and Raoul R Wadhwa and Nara Yoon and Daniel Nichol and Andriy Marusyk and Michael Hinczewski and Jacob G Scott},\r\n journal={Physical {B}iology},\r\n volume={16},\r\n number={4},\r\n pages={041005},\r\n year={2019},\r\n publisher={IOP Publishing},\r\n abstract={Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.}\r\n}\r\n\r\n\n\n Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.\n
\n\n\n\n \n\n \n \n \n \n \n Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer.\n \n \n \n\n\n \n Kaznatcheev, A.; Peacock, J.; Basanta, D.; Marusyk, A.; and Scott, J. G\n\n\n \n\n\n\n Nature Ecology & Evolution, 3(3): 450. 2019.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2019fibroblasts,\r\n title={Fibroblasts and alectinib switch the evolutionary games played by non-small cell lung cancer},\r\n author={Kaznatcheev, Artem and Peacock, Jeffrey and Basanta, David and Marusyk, Andriy and Scott, Jacob G},\r\n journal={Nature Ecology \\& Evolution},\r\n volume={3},\r\n number={3},\r\n pages={450},\r\n year={2019},\r\n publisher={Nature Publishing Group},\r\n abstract={Heterogeneity in strategies for survival and proliferation among the cells that constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumour’s strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells that are sensitive and resistant to the anaplastic lymphoma kinase inhibitor alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer-associated fibroblasts qualitatively switch the type of game being played by the in vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.}\r\n}\r\n\r\n\n\n Heterogeneity in strategies for survival and proliferation among the cells that constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumour’s strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells that are sensitive and resistant to the anaplastic lymphoma kinase inhibitor alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer-associated fibroblasts qualitatively switch the type of game being played by the in vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.\n
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\n \n \n\n \n \n \n \n \n Effective games and the confusion over spatial structure.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n Proceedings of the National Academy of Sciences, 115(8): E1709–E1709. 2018.\n \n\n\n\n
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\n\n\n\n@article{kaznatcheev2018effective,\r\n title={Effective games and the confusion over spatial structure},\r\n author={Kaznatcheev, Artem},\r\n journal={Proceedings of the National Academy of Sciences},\r\n volume={115},\r\n number={8},\r\n pages={E1709--E1709},\r\n year={2018},\r\n publisher={National Academy of Sciences}\r\n}\r\n\r\n\n\n \n\n \n \n \n \n \n Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer.\n \n \n \n\n\n \n Warman, P. I; Kaznatcheev, A.; Araujo, A.; Lynch, C. C; and Basanta, D.\n\n\n \n\n\n\n Games, 9(2): 19. 2018.\n \n\n\n\n
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\n\n\n@article{warman2018fractionated,\r\n title={Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer},\r\n author={Warman, Pranav I and Kaznatcheev, Artem and Araujo, Arturo and Lynch, Conor C and Basanta, David},\r\n journal={Games},\r\n volume={9},\r\n number={2},\r\n pages={19},\r\n year={2018},\r\n publisher={Multidisciplinary Digital Publishing Institute},\r\n abstract={Prostate cancer to bone metastases are almost always lethal. This results from the ability of metastatic prostate cancer cells to co-opt bone remodeling, leading to what is known as the vicious cycle. Understanding how tumor cells can disrupt bone homeostasis through their interactions with the stroma and how metastatic tumors respond to treatment is key to the development of new treatments for what remains an incurable disease. Here we describe an evolutionary game theoretical model of both the homeostatic bone remodeling and its co-option by prostate cancer metastases. This model extends past the evolutionary aspects typically considered in game theoretical models by also including ecological factors such as the physical microenvironment of the bone. Our model recapitulates the current paradigm of the “vicious cycle” driving tumor growth and sheds light on the interactions of heterogeneous tumor cells with the bone microenvironment and treatment response. Our results show that resistant populations naturally become dominant in the metastases under conventional cytotoxic treatment and that novel schedules could be used to better control the tumor and the associated bone disease compared to the current standard of care. Specifically, we introduce fractionated follow up therapy—chemotherapy where dosage is administered initially in one solid block followed by alternating smaller doses and holidays—and argue that it is better than either a continuous application or a periodic one. Furthermore, we also show that different regimens of chemotherapy can lead to different amounts of pathological bone that are known to correlate with poor quality of life for bone metastatic prostate cancer patients.}\r\n}\r\n\r\n\n\n Prostate cancer to bone metastases are almost always lethal. This results from the ability of metastatic prostate cancer cells to co-opt bone remodeling, leading to what is known as the vicious cycle. Understanding how tumor cells can disrupt bone homeostasis through their interactions with the stroma and how metastatic tumors respond to treatment is key to the development of new treatments for what remains an incurable disease. Here we describe an evolutionary game theoretical model of both the homeostatic bone remodeling and its co-option by prostate cancer metastases. This model extends past the evolutionary aspects typically considered in game theoretical models by also including ecological factors such as the physical microenvironment of the bone. Our model recapitulates the current paradigm of the “vicious cycle” driving tumor growth and sheds light on the interactions of heterogeneous tumor cells with the bone microenvironment and treatment response. Our results show that resistant populations naturally become dominant in the metastases under conventional cytotoxic treatment and that novel schedules could be used to better control the tumor and the associated bone disease compared to the current standard of care. Specifically, we introduce fractionated follow up therapy—chemotherapy where dosage is administered initially in one solid block followed by alternating smaller doses and holidays—and argue that it is better than either a continuous application or a periodic one. Furthermore, we also show that different regimens of chemotherapy can lead to different amounts of pathological bone that are known to correlate with poor quality of life for bone metastatic prostate cancer patients.\n
\n\n\n\n \n\n \n \n \n \n \n Moral externalization may precede, not follow, subjective preferences.\n \n \n \n\n\n \n Kaznatcheev, A.; and Shultz, T. R\n\n\n \n\n\n\n Behavioral and Brain Sciences, 41. 2018.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2018moral,\r\n title={Moral externalization may precede, not follow, subjective preferences},\r\n author={Kaznatcheev, Artem and Shultz, Thomas R},\r\n journal={Behavioral and Brain Sciences},\r\n volume={41},\r\n year={2018},\r\n publisher={Cambridge University Press},\r\n abstract={We offer four counterarguments against Stanford's dismissal of moral externalization as an ancestral condition, based on requirements for ancestral states, mismatch between theoretical and empirical games, passively correlated interactions, and social interfaces that prevent agents' knowing game payoffs. The fact that children's externalized phenomenology precedes their discovery of subjectivized phenomenology also suggests that externalized phenomenology is an ancestral condition.}\r\n}\r\n\r\n\n\n We offer four counterarguments against Stanford's dismissal of moral externalization as an ancestral condition, based on requirements for ancestral states, mismatch between theoretical and empirical games, passively correlated interactions, and social interfaces that prevent agents' knowing game payoffs. The fact that children's externalized phenomenology precedes their discovery of subjectivized phenomenology also suggests that externalized phenomenology is an ancestral condition.\n
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\n\n \n \n 2017\n \n \n (3)\n \n \n
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\n \n \n\n \n \n \n \n \n Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature.\n \n \n \n\n\n \n Kaznatcheev, A.; Vander Velde, R.; Scott, J. G.; and Basanta, D.\n\n\n \n\n\n\n British Journal of Cancer, 116: 785–792. 2017.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2017cancer,\r\n title={Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature},\r\n author={Kaznatcheev, Artem and Vander Velde, Robert and Scott, Jacob G. and Basanta, David},\r\n journal={British Journal of Cancer},\r\n volume={116},\r\n pages={785--792},\r\n year={2017},\r\n abstract={Background:\r\n Tumours are diverse ecosystems with persistent heterogeneity in various cancer hallmarks like self-sufficiency of growth factor production for angiogenesis and reprogramming of energy metabolism for aerobic glycolysis. This heterogeneity has consequences for diagnosis, treatment and disease progression.\r\n \r\n Methods:\r\n We introduce the double goods game to study the dynamics of these traits using evolutionary game theory. We model glycolytic acid production as a public good for all tumour cells and oxygen from vascularisation via vascular endothelial growth factor production as a club good benefiting non-glycolytic tumour cells. This results in three viable phenotypic strategies: glycolytic, angiogenic and aerobic non-angiogenic.\r\n \r\n Results:\r\n We classify the dynamics into three qualitatively distinct regimes: (1) fully glycolytic; (2) fully angiogenic; or (3) polyclonal in all three cell types. The third regime allows for dynamic heterogeneity even with linear goods, something that was not possible in prior public good models that considered glycolysis or growth factor production in isolation.\r\n \r\n Conclusions:\r\n The cyclic dynamics of the polyclonal regime stress the importance of timing for anti-glycolysis treatments like lonidamine. The existence of qualitatively different dynamic regimes highlights the order effects of treatments. In particular, we consider the potential of vascular normalisation as a neoadjuvant therapy before follow-up with interventions like buffer therapy.}\r\n}\r\n\r\n\n\n Background: Tumours are diverse ecosystems with persistent heterogeneity in various cancer hallmarks like self-sufficiency of growth factor production for angiogenesis and reprogramming of energy metabolism for aerobic glycolysis. This heterogeneity has consequences for diagnosis, treatment and disease progression. Methods: We introduce the double goods game to study the dynamics of these traits using evolutionary game theory. We model glycolytic acid production as a public good for all tumour cells and oxygen from vascularisation via vascular endothelial growth factor production as a club good benefiting non-glycolytic tumour cells. This results in three viable phenotypic strategies: glycolytic, angiogenic and aerobic non-angiogenic. Results: We classify the dynamics into three qualitatively distinct regimes: (1) fully glycolytic; (2) fully angiogenic; or (3) polyclonal in all three cell types. The third regime allows for dynamic heterogeneity even with linear goods, something that was not possible in prior public good models that considered glycolysis or growth factor production in isolation. Conclusions: The cyclic dynamics of the polyclonal regime stress the importance of timing for anti-glycolysis treatments like lonidamine. The existence of qualitatively different dynamic regimes highlights the order effects of treatments. In particular, we consider the potential of vascular normalisation as a neoadjuvant therapy before follow-up with interventions like buffer therapy.\n
\n\n\n\n \n\n \n \n \n \n \n Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia.\n \n \n \n\n\n \n Kaznatcheev, A.; Grimes, D. R.; Vander Velde, R.; Cannataro, V.; Baratchart, E.; Dhawan, A.; Liu, L.; Myroshnychenko, D.; Taylor-King, J. P; Yoon, N.; Padron, E.; Marusyk, A.; and Basanta, D.\n\n\n \n\n\n\n bioRxiv,211151. 2017.\n \n\n\n\n
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\n\n\n@article{IMO2016,\r\n title={Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia},\r\n author={Kaznatcheev, Artem and Grimes, David Robert and Vander Velde, Robert and Cannataro, Vincent and Baratchart, Etienne and Dhawan, Andrew and Liu, Lin and Myroshnychenko, Daria and Taylor-King, Jake P and Yoon, Nara and Padron, Eric and Marusyk, Andriy and Basanta, David},\r\n journal={bioRxiv},\r\n pages={211151},\r\n year={2017},\r\n publisher={Cold Spring Harbor Laboratory},\r\n abstract={Acquired therapy resistance to cancer treatment is a common and serious clinical problem. The classic U-shape model for the emergence of resistance supposes that: (1) treatment changes the selective pressure on the treatment-naive tumour; (2) this shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant; (3) the resistant cells then out-compete the sensitive cells and – if further interventions (like drug holidays or new drugs or dosage changes) are not pursued – take over the tumour: returning it to a state dangerous to the patient. The emergence of ruxolitinib resistance in chronic myelomonocytic leukemia (CMML) seems to challenge the classic model: we see the global properties of resistance, but not the drastic change in clonal architecture expected with the selection bottleneck. To study this, we explore three population-level models as alternatives to the classic model of resistance. These three effective models are designed in such a way that they are distinguishable based on limited experimental data on the time-progression of resistance in CMML. We also propose a candidate reductive implementation of the proximal cause of resistance to ground these effective theories. With these reductive implementations in mind, we also explore the impact of oxygen diffusion and spatial structure more generally on the dynamics of CMML in the bone marrow concluding that, even small fluctuations in oxygen availability can seriously impact the efficacy of ruxolitinib. Finally, we look at the ability of spatially distributed cytokine signaling feedback loops to produce a relapse in symptoms similar to what we observe in the clinic.}\r\n}\r\n\r\n\n\n Acquired therapy resistance to cancer treatment is a common and serious clinical problem. The classic U-shape model for the emergence of resistance supposes that: (1) treatment changes the selective pressure on the treatment-naive tumour; (2) this shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant; (3) the resistant cells then out-compete the sensitive cells and – if further interventions (like drug holidays or new drugs or dosage changes) are not pursued – take over the tumour: returning it to a state dangerous to the patient. The emergence of ruxolitinib resistance in chronic myelomonocytic leukemia (CMML) seems to challenge the classic model: we see the global properties of resistance, but not the drastic change in clonal architecture expected with the selection bottleneck. To study this, we explore three population-level models as alternatives to the classic model of resistance. These three effective models are designed in such a way that they are distinguishable based on limited experimental data on the time-progression of resistance in CMML. We also propose a candidate reductive implementation of the proximal cause of resistance to ground these effective theories. With these reductive implementations in mind, we also explore the impact of oxygen diffusion and spatial structure more generally on the dynamics of CMML in the bone marrow concluding that, even small fluctuations in oxygen availability can seriously impact the efficacy of ruxolitinib. Finally, we look at the ability of spatially distributed cytokine signaling feedback loops to produce a relapse in symptoms similar to what we observe in the clinic.\n
\n\n\n\n \n\n \n \n \n \n \n Two conceptions of evolutionary games: reductive vs effective.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n bioRxiv,231993. 2017.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2017two,\r\n title={Two conceptions of evolutionary games: reductive vs effective},\r\n author={Kaznatcheev, Artem},\r\n journal={bioRxiv},\r\n pages={231993},\r\n year={2017},\r\n publisher={Cold Spring Harbor Laboratory},\r\n abstract={Evolutionary game theory (EGT) was born from economic game theory through a series of analogies. Given this heuristic genealogy, a number of central objects of the theory (like strategies, players, and games) have not been carefully defined or interpreted. A specific interpretation of these terms becomes important as EGT sees more applications to understanding experiments in microscopic systems typical of oncology and microbiology. In this essay, I provide two interpretations of the central objects of games theory: one that leads to reductive games and the other to effective games. These interpretation are based on the difference between views of fitness as a property of individuals versus fitness as a summary statistic of (sub)populations. Reductive games are typical of theoretical work like agent-based models. But effective games usually correspond more closely to experimental work. However, confusing reductive games for effective games or vice-versa can lead to divergent results, especially in spatially structured populations. As such, I propose that we treat this distinction carefully in future work at the interface of EGT and experiment.}\r\n}\r\n\r\n\n\n Evolutionary game theory (EGT) was born from economic game theory through a series of analogies. Given this heuristic genealogy, a number of central objects of the theory (like strategies, players, and games) have not been carefully defined or interpreted. A specific interpretation of these terms becomes important as EGT sees more applications to understanding experiments in microscopic systems typical of oncology and microbiology. In this essay, I provide two interpretations of the central objects of games theory: one that leads to reductive games and the other to effective games. These interpretation are based on the difference between views of fitness as a property of individuals versus fitness as a summary statistic of (sub)populations. Reductive games are typical of theoretical work like agent-based models. But effective games usually correspond more closely to experimental work. However, confusing reductive games for effective games or vice-versa can lead to divergent results, especially in spatially structured populations. As such, I propose that we treat this distinction carefully in future work at the interface of EGT and experiment.\n
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\n\n \n \n 2016\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement.\n \n \n \n\n\n \n Hanson, S.; Grimes, D. R.; Taylor-King, J. P; Bauer, B.; Warman, P. I; Frankenstein, Z.; Kaznatcheev, A.; Bonassar, M. J; Cannataro, V. L; Motawe, Z. Y; Lima, E. A B F; Kim, S.; Davila, M. L; and Araujo, A.\n\n\n \n\n\n\n BioRxiv,049908. 2016.\n \n\n\n\n
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\n\n\n@article{IMO2015,\r\n title={Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement},\r\n author={Hanson, Shalla and Grimes, David Robert and Taylor-King, Jake P and Bauer, Benedikt and Warman, Pravnam I and Frankenstein, Ziv and Kaznatcheev, Artem and Bonassar, Michael J and Cannataro, Vincent L and Motawe, Zeinab Y and Lima, Ernesto A B F and Kim, Sungjune and Davila, Marco L and Araujo, Arturo},\r\n journal={BioRxiv},\r\n pages={049908},\r\n year={2016},\r\n publisher={Cold Spring Harbor Laboratory},\r\n abstract={Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic.\r\n\r\n To inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients’ tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients.\r\n\r\n We outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.}\r\n}\r\n\r\n\n\n Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic. To inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients’ tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients. We outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.\n
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\n\n \n \n 2015\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n Edge effects in game-theoretic dynamics of spatially structured tumours.\n \n \n \n\n\n \n Kaznatcheev, A.; Scott, J. G; and Basanta, D.\n\n\n \n\n\n\n Journal of The Royal Society Interface, 12(108): 20150154. 2015.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2015edge,\r\n title={Edge effects in game-theoretic dynamics of spatially structured tumours},\r\n author={Kaznatcheev, Artem and Scott, Jacob G and Basanta, David},\r\n journal={Journal of The Royal Society Interface},\r\n volume={12},\r\n number={108},\r\n pages={20150154},\r\n year={2015},\r\n publisher={The Royal Society},\r\n abstract={Cancer dynamics are an evolutionary game between cellular phenotypes. A typical assumption in this modelling paradigm is that the probability of a given phenotypic strategy interacting with another depends exclusively on the abundance of those strategies without regard for local neighbourhood structure. We address this limitation by using the Ohtsuki–Nowak transform to introduce spatial structure to the go versus grow game. We show that spatial structure can promote the invasive (go) strategy. By considering the change in neighbourhood size at a static boundary—such as a blood vessel, organ capsule or basement membrane—we show an edge effect that allows a tumour without invasive phenotypes in the bulk to have a polyclonal boundary with invasive cells. We present an example of this promotion of invasive (epithelial–mesenchymal transition-positive) cells in a metastatic colony of prostate adenocarcinoma in bone marrow. Our results caution that pathologic analyses that do not distinguish between cells in the bulk and cells at a static edge of a tumour can underestimate the number of invasive cells. Although we concentrate on applications in mathematical oncology, we expect our approach to extend to other evolutionary game models where interaction neighbourhoods change at fixed system boundaries.}\r\n}\r\n\r\n\n\n Cancer dynamics are an evolutionary game between cellular phenotypes. A typical assumption in this modelling paradigm is that the probability of a given phenotypic strategy interacting with another depends exclusively on the abundance of those strategies without regard for local neighbourhood structure. We address this limitation by using the Ohtsuki–Nowak transform to introduce spatial structure to the go versus grow game. We show that spatial structure can promote the invasive (go) strategy. By considering the change in neighbourhood size at a static boundary—such as a blood vessel, organ capsule or basement membrane—we show an edge effect that allows a tumour without invasive phenotypes in the bulk to have a polyclonal boundary with invasive cells. We present an example of this promotion of invasive (epithelial–mesenchymal transition-positive) cells in a metastatic colony of prostate adenocarcinoma in bone marrow. Our results caution that pathologic analyses that do not distinguish between cells in the bulk and cells at a static edge of a tumour can underestimate the number of invasive cells. Although we concentrate on applications in mathematical oncology, we expect our approach to extend to other evolutionary game models where interaction neighbourhoods change at fixed system boundaries.\n
\n\n\n\n \n\n \n \n \n \n \n Fidelity drive: A mechanism for chaperone proteins to maintain stable mutation rates in prokaryotes over evolutionary time.\n \n \n \n\n\n \n Xue, J. Z; Kaznatcheev, A.; Costopoulos, A.; and Guichard, F.\n\n\n \n\n\n\n Journal of theoretical biology, 364: 162–167. 2015.\n \n\n\n\n
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\n\n\n@article{xue2015fidelity,\r\n title={Fidelity drive: A mechanism for chaperone proteins to maintain stable mutation rates in prokaryotes over evolutionary time},\r\n author={Xue, Julian Z and Kaznatcheev, Artem and Costopoulos, Andre and Guichard, Frederic},\r\n journal={Journal of theoretical biology},\r\n volume={364},\r\n pages={162--167},\r\n year={2015},\r\n publisher={Academic Press},\r\n abstract={We show a mechanism by which chaperone proteins can play a key role in maintaining the long-term evolutionary stability of mutation rates in prokaryotes with perfect genetic linkage. Since chaperones can reduce the phenotypic effects of mutations, higher mutation rate, by affecting chaperones, can increase the phenotypic effects of mutations. This in turn leads to greater mutation effect among the proteins that control mutation repair and DNA replication, resulting in large changes in mutation rate. The converse of this is that when mutation rate is low and chaperones are functioning well, then the rate of change in mutation rate will also be low, leading to low mutation rates being evolutionarily frozen. We show that the strength of this recursion is critical to determining the long-term evolutionary patterns of mutation rate among prokaryotes. If this recursion is weak, then mutation rates can grow without bound, leading to the extinction of the lineage. However, if this recursion is strong, then we can reproduce empirical patterns of prokaryotic mutation rates, where mutation rates remain stable over evolutionary time, and where most mutation rates are low, but with a significant fraction of high mutators.}\r\n}\r\n\r\n\n\n We show a mechanism by which chaperone proteins can play a key role in maintaining the long-term evolutionary stability of mutation rates in prokaryotes with perfect genetic linkage. Since chaperones can reduce the phenotypic effects of mutations, higher mutation rate, by affecting chaperones, can increase the phenotypic effects of mutations. This in turn leads to greater mutation effect among the proteins that control mutation repair and DNA replication, resulting in large changes in mutation rate. The converse of this is that when mutation rate is low and chaperones are functioning well, then the rate of change in mutation rate will also be low, leading to low mutation rates being evolutionarily frozen. We show that the strength of this recursion is critical to determining the long-term evolutionary patterns of mutation rate among prokaryotes. If this recursion is weak, then mutation rates can grow without bound, leading to the extinction of the lineage. However, if this recursion is strong, then we can reproduce empirical patterns of prokaryotic mutation rates, where mutation rates remain stable over evolutionary time, and where most mutation rates are low, but with a significant fraction of high mutators.\n
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\n\n \n \n 2014\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Evolving useful delusions: Subjectively rational selfishness leads to objectively irrational cooperation.\n \n \n \n\n\n \n Kaznatcheev, A.; Montrey, M.; and Shultz, T. R\n\n\n \n\n\n\n In Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014. \n \n\n\n\n
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\n\n\n@inproceedings{kaznatcheev2014evolving,\r\n title={Evolving useful delusions: Subjectively rational selfishness leads to objectively irrational cooperation},\r\n author={Kaznatcheev, Artem and Montrey, Marcel and Shultz, Thomas R},\r\n booktitle={Proceedings of the 36th Annual Meeting of the Cognitive Science Society},\r\n year={2014},\r\n abstract={We introduce a framework within evolutionary game theory for studying the distinction between objective and subjective rationality and apply it to the evolution of cooperation on 3-regular random graphs. In our simulations, agents evolve misrepresentations of objective reality that help them cooperate and maintain higher social welfare in the Prisoner's dilemma. These agents act rationally on their subjective representations of the world, but irrationally from the perspective of an external observer. We model misrepresentations as subjective perceptions of payoffs and quasi-magical thinking as an inferential bias, finding that the former is more conducive to cooperation. This highlights the importance of internal representations, not just observed behavior, in evolutionary thought. Our results provide support for the interface theory of perception and suggest that the individual's interface can serve not only the individual's aims, but also society as a whole, offering insight into social phenomena such as religion.}\r\n}\r\n\r\n\n\n We introduce a framework within evolutionary game theory for studying the distinction between objective and subjective rationality and apply it to the evolution of cooperation on 3-regular random graphs. In our simulations, agents evolve misrepresentations of objective reality that help them cooperate and maintain higher social welfare in the Prisoner's dilemma. These agents act rationally on their subjective representations of the world, but irrationally from the perspective of an external observer. We model misrepresentations as subjective perceptions of payoffs and quasi-magical thinking as an inferential bias, finding that the former is more conducive to cooperation. This highlights the importance of internal representations, not just observed behavior, in evolutionary thought. Our results provide support for the interface theory of perception and suggest that the individual's interface can serve not only the individual's aims, but also society as a whole, offering insight into social phenomena such as religion.\n
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\n\n \n \n 2013\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n Limitations of the Dirac formalism as a descriptive framework for cognition.\n \n \n \n\n\n \n Kaznatcheev, A.; and Shultz, T. R\n\n\n \n\n\n\n Behavioral and Brain Sciences, 36(3): 292. 2013.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2013limitations,\r\n title={Limitations of the Dirac formalism as a descriptive framework for cognition},\r\n author={Kaznatcheev, Artem and Shultz, Thomas R},\r\n journal={Behavioral and Brain Sciences},\r\n volume={36},\r\n number={3},\r\n pages={292},\r\n year={2013},\r\n publisher={Cambridge University Press},\r\n abstract={We highlight methodological and theoretical limitations of the authors' Dirac formalism and suggest the von Neumann open systems approach as a resolution. The open systems framework is a generalization of classical probability and we hope it will allow cognitive scientists to extend quantum probability from perception, categorization, memory, decision making, and similarity judgments to phenomena in learning and development.}\r\n}\r\n\r\n\n\n We highlight methodological and theoretical limitations of the authors' Dirac formalism and suggest the von Neumann open systems approach as a resolution. The open systems framework is a generalization of classical probability and we hope it will allow cognitive scientists to extend quantum probability from perception, categorization, memory, decision making, and similarity judgments to phenomena in learning and development.\n
\n\n\n\n \n\n \n \n \n \n \n The evolutionary dominance of ethnocentric cooperation.\n \n \n \n\n\n \n Hartshorn, M.; Kaznatcheev, A.; and Shultz, T.\n\n\n \n\n\n\n Journal of Artificial Societies and Social Simulation, 16(3): 7. 2013.\n \n\n\n\n
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\n\n\n@article{hartshorn2013evolutionary,\r\n title={The evolutionary dominance of ethnocentric cooperation},\r\n author={Hartshorn, Max and Kaznatcheev, Artem and Shultz, Thomas},\r\n journal={Journal of Artificial Societies and Social Simulation},\r\n volume={16},\r\n number={3},\r\n pages={7},\r\n year={2013},\r\n abstract={Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates. Selfish and traitorous strategies are self-limiting because such agents do not cooperate with agents sharing the same genes. Traitorous strategies fare even worse than selfish ones because traitors are exploited by ethnocentrics across group boundaries in the same manner as humanitarians are, via unreciprocated cooperation. By tracking evolution across time, we find individual differences between evolving worlds in terms of early humanitarian competition with ethnocentrism, including early stages of humanitarian dominance. Our evidence indicates that such variation, in terms of differences between humanitarian and ethnocentric agents, is normally distributed and due to early, rather than later, stochastic differences in immigrant strategies.}\r\n}\r\n\r\n\n\n Recent agent-based computer simulations suggest that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution. From a random start, ethnocentric strategies dominate other possible strategies (selfish, traitorous, and humanitarian) based on cooperation or non-cooperation with in-group and out-group agents. Here we show that ethnocentrism eventually overcomes its closest competitor, humanitarianism, by exploiting humanitarian cooperation across group boundaries as world population saturates. Selfish and traitorous strategies are self-limiting because such agents do not cooperate with agents sharing the same genes. Traitorous strategies fare even worse than selfish ones because traitors are exploited by ethnocentrics across group boundaries in the same manner as humanitarians are, via unreciprocated cooperation. By tracking evolution across time, we find individual differences between evolving worlds in terms of early humanitarian competition with ethnocentrism, including early stages of humanitarian dominance. Our evidence indicates that such variation, in terms of differences between humanitarian and ethnocentric agents, is normally distributed and due to early, rather than later, stochastic differences in immigrant strategies.\n
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\n\n \n \n 2011\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Ethnocentrism Maintains Cooperation, but Keeping One’s Children Close Fuels It.\n \n \n \n\n\n \n Kaznatcheev, A.; and Shultz, T. R\n\n\n \n\n\n\n In Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, 2011. \n \n\n\n\n
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\n\n\n@inproceedings{kaznatcheev2011ethnocentrism,\r\n title={Ethnocentrism Maintains Cooperation, but Keeping One’s Children Close Fuels It},\r\n author={Kaznatcheev, Artem and Shultz, Thomas R},\r\n booktitle={Proceedings of the 33rd Annual Meeting of the Cognitive Science Society},\r\n year={2011},\r\n abstract={Ethnocentrism, commonly thought to rely on complex social cognition, can arise through biological evolution in populations with minimal cognitive abilities. In fact, ethnocentrism is considered to be one of the simplest mechanisms for establishing cooperation in the competitive environment of natural selection. Here we study a recent agent-based model. Through our simulations and analysis, we establish that the mechanism responsible for the emergence of cooperation is children residing close to their parents. Our results suggest that group tags maintain cooperation, but do not create it. We formalize this observation as the dual direct hypothesis: ethnocentric agents dominate humanitarian agents by exploiting the unconditional cooperation of humanitarians of different tags to maintain the number of ethnocentric agents after world saturation. We affirm previous observations on the importance of world saturation, finding its drastic effect on dynamics in both spatial tagbased and tag-less models.}\r\n}\r\n\r\n\n\n Ethnocentrism, commonly thought to rely on complex social cognition, can arise through biological evolution in populations with minimal cognitive abilities. In fact, ethnocentrism is considered to be one of the simplest mechanisms for establishing cooperation in the competitive environment of natural selection. Here we study a recent agent-based model. Through our simulations and analysis, we establish that the mechanism responsible for the emergence of cooperation is children residing close to their parents. Our results suggest that group tags maintain cooperation, but do not create it. We formalize this observation as the dual direct hypothesis: ethnocentric agents dominate humanitarian agents by exploiting the unconditional cooperation of humanitarians of different tags to maintain the number of ethnocentric agents after world saturation. We affirm previous observations on the importance of world saturation, finding its drastic effect on dynamics in both spatial tagbased and tag-less models.\n
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\n\n \n \n 2010\n \n \n (5)\n \n \n
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\n \n \n\n \n \n \n \n \n The cognitive cost of ethnocentrism.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n In Proceedings of the 32nd Annual Meeting of the Cognitive Science Society, 2010. \n \n\n\n\n
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\n\n\n@inproceedings{kaznatcheev2010cognitive,\r\n title={The cognitive cost of ethnocentrism},\r\n author={Kaznatcheev, Artem},\r\n booktitle={Proceedings of the 32nd Annual Meeting of the Cognitive Science Society},\r\n year={2010},\r\n abstract={Recent computational studies suggest that ethnocentrism, commonly thought to rely on complex social cognition, may arise through biological evolution in populations with minimal cognitive abilities. We use the methods of evolutionary game theory and computational modelling to examine the evolution of ethnocentrism. Since ethnocentric agents differentiate between in-and out-group partners, and adjust their behavior accordingly, they are more cognitively complex than humanitarian or selfish agents that always cooperate or defect, respectively. We associate a fitness cost with this complexity and test the robustness of ethnocentrism, concluding that ethnocentrism is not robust against increases in cost of cognition. Our model confirms that humanitarians are suppressed largely by ethnocentrics. Paradoxically, we observe that the proportion of cooperation is higher in worlds dominated by ethnocentrics. We conclude that suppressing free-riders, such as selfish and traitorous agents, allows ethnocentrics to maintain higher levels of cooperative interactions.}\r\n}\r\n\r\n\n\n Recent computational studies suggest that ethnocentrism, commonly thought to rely on complex social cognition, may arise through biological evolution in populations with minimal cognitive abilities. We use the methods of evolutionary game theory and computational modelling to examine the evolution of ethnocentrism. Since ethnocentric agents differentiate between in-and out-group partners, and adjust their behavior accordingly, they are more cognitively complex than humanitarian or selfish agents that always cooperate or defect, respectively. We associate a fitness cost with this complexity and test the robustness of ethnocentrism, concluding that ethnocentrism is not robust against increases in cost of cognition. Our model confirms that humanitarians are suppressed largely by ethnocentrics. Paradoxically, we observe that the proportion of cooperation is higher in worlds dominated by ethnocentrics. We conclude that suppressing free-riders, such as selfish and traitorous agents, allows ethnocentrics to maintain higher levels of cooperative interactions.\n
\n\n\n\n \n\n \n \n \n \n \n A connectionist study on the interplay of nouns and pronouns in personal pronoun acquisition.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n Cognitive Computation, 2(4): 280–284. 2010.\n \n\n\n\n
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\n\n\n@article{kaznatcheev2010connectionist,\r\n title={A connectionist study on the interplay of nouns and pronouns in personal pronoun acquisition},\r\n author={Kaznatcheev, Artem},\r\n journal={Cognitive Computation},\r\n volume={2},\r\n number={4},\r\n pages={280--284},\r\n year={2010},\r\n publisher={Springer-Verlag},\r\n abstract={Cascade-correlation learning is used to model pronoun acquisition in children. The cascade-correlation algorithm is a feed-forward neural network that builds its own topology from input and output units. Personal pronoun acquisition is an interesting non-linear problem in psychology. A mother will refer to her son as you and herself as me, but the son must infer for himself that when he speaks to his mother, she becomes you and he becomes me. Learning the shifting reference of these pronouns is a difficult task that most children master. We show that learning of two different noun-and-pronoun addressee patterns is consistent with naturalistic studies. We observe a surprising factor in pronoun reversal: increasing the amount of exposure to noun patterns can decrease or eliminate reversal errors in children.}\r\n}\r\n\r\n\n\n Cascade-correlation learning is used to model pronoun acquisition in children. The cascade-correlation algorithm is a feed-forward neural network that builds its own topology from input and output units. Personal pronoun acquisition is an interesting non-linear problem in psychology. A mother will refer to her son as you and herself as me, but the son must infer for himself that when he speaks to his mother, she becomes you and he becomes me. Learning the shifting reference of these pronouns is a difficult task that most children master. We show that learning of two different noun-and-pronoun addressee patterns is consistent with naturalistic studies. We observe a surprising factor in pronoun reversal: increasing the amount of exposure to noun patterns can decrease or eliminate reversal errors in children.\n
\n\n\n\n \n\n \n \n \n \n \n Robustness of Ethnocentrism to Changes in Interpersonal Interactions.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n In AAAI Fall Symposium: Complex Adaptive Systems, 2010. \n \n\n\n\n
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\n\n\n@inproceedings{kaznatcheev2010robustness,\r\n title={Robustness of Ethnocentrism to Changes in Interpersonal Interactions.},\r\n author={Kaznatcheev, Artem},\r\n booktitle={AAAI Fall Symposium: Complex Adaptive Systems},\r\n year={2010},\r\n abstract={We use the methods of evolutionary game theory and computational modelling to examine the evolution of ethnocentrism. We show that ethnocentrism evolves in a spatially structured population not only under prisoner’s dilemma interactions, but also hawk-dove, assurance, harmony, and leader games. In the case of harmony, ethnocentrism evolves even when defection is irrational. This suggests that the pressure of competing for a common resource (in our model: free space) can produce irrational hostility between groups. The minimal cognitive assumptions in our model also suggest that the ethnocentrism observed in humans and elsewhere in nature has an evolutionary basis that is robust over changes in interaction types.}\r\n}\r\n\r\n\n\n We use the methods of evolutionary game theory and computational modelling to examine the evolution of ethnocentrism. We show that ethnocentrism evolves in a spatially structured population not only under prisoner’s dilemma interactions, but also hawk-dove, assurance, harmony, and leader games. In the case of harmony, ethnocentrism evolves even when defection is irrational. This suggests that the pressure of competing for a common resource (in our model: free space) can produce irrational hostility between groups. The minimal cognitive assumptions in our model also suggest that the ethnocentrism observed in humans and elsewhere in nature has an evolutionary basis that is robust over changes in interaction types.\n
\n\n\n\n \n\n \n \n \n \n \n Structure of exact and approximate unitary t-designs.\n \n \n \n\n\n \n Kaznatcheev, A.\n\n\n \n\n\n\n 2010.\n \n\n\n\n
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\n\n\n@misc{kaznatcheev2010structure,\r\n title={Structure of exact and approximate unitary t-designs},\r\n author={Kaznatcheev, Artem},\r\n year={2010},\r\n abstract={When studying “random” operators it is essential to be able to integrate over the Haar measure, both analytically and algorithmically. Unitary t-designs provide a method to simplify integrating polynomials of degree less than t over U(d). In particular, by replacing averages over the Haar measure by averages over a finite set, they allow applications in algorithms. We provide three equivalent definitions for unitary t-designs and introduce group and approximate designs. The main tool in this note is our generalization of an important result-the trace double sum inequality-into the trace 2p-sum inequality. We use the trace double sum inequality to produce a correspondence between minimal designs and unique minimal weight functions. We culminate our exploration of the structure of t-designs by showing that t-designs span {U⊗ t| U∈ U (d)}. This result produces two conjectures which we believe are an important step in the classification of minimum unitary t-designs.}\r\n}\r\n\r\n\n\n When studying “random” operators it is essential to be able to integrate over the Haar measure, both analytically and algorithmically. Unitary t-designs provide a method to simplify integrating polynomials of degree less than t over U(d). In particular, by replacing averages over the Haar measure by averages over a finite set, they allow applications in algorithms. We provide three equivalent definitions for unitary t-designs and introduce group and approximate designs. The main tool in this note is our generalization of an important result-the trace double sum inequality-into the trace 2p-sum inequality. We use the trace double sum inequality to produce a correspondence between minimal designs and unique minimal weight functions. We culminate our exploration of the structure of t-designs by showing that t-designs span U⊗ t| U∈ U (d). This result produces two conjectures which we believe are an important step in the classification of minimum unitary t-designs.\n
\n\n\n\n \n\n \n \n \n \n \n Self-esteem and the Matching Effect in Mate Selection.\n \n \n \n\n\n \n Kaznatcheev, A.; Brown, K.; and Shultz, T. R\n\n\n \n\n\n\n In Proceedings of the 32nd Annual Meeting of the Cognitive Science Society, 2010. \n \n\n\n\n
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\n\n\n@inproceedings{kaznatcheev2010self,\r\n title={Self-esteem and the Matching Effect in Mate Selection},\r\n author={Kaznatcheev, Artem and Brown, Kyler and Shultz, Thomas R},\r\n booktitle={Proceedings of the 32nd Annual Meeting of the Cognitive Science Society},\r\n year={2010},\r\n abstract={The matching effect is the empirical finding that romantic couples have a high correlation in physical attractiveness. It remains a debate as to whether this correlation is based purely on similarity preference-the matching hypothesis-or marketplace forces. We present a new marketplace model for romantic relationships. Previous models granted every person access to his/her own attractiveness. In reality, people have only a vague idea of their own attractiveness ratings. We introduce a concept analogous to self-esteem to model this phenomenon. Further, we extend beyond previous models by dealing explicitly with both the initialization and development of a relationship. Our model accounts for the experimental tendency to choose more attractive partners, while still explaining observed intra-couple attractiveness correlation and the difference in correlation between casual and serious daters.}\r\n}\r\n\r\n\n\n The matching effect is the empirical finding that romantic couples have a high correlation in physical attractiveness. It remains a debate as to whether this correlation is based purely on similarity preference-the matching hypothesis-or marketplace forces. We present a new marketplace model for romantic relationships. Previous models granted every person access to his/her own attractiveness. In reality, people have only a vague idea of their own attractiveness ratings. We introduce a concept analogous to self-esteem to model this phenomenon. Further, we extend beyond previous models by dealing explicitly with both the initialization and development of a relationship. Our model accounts for the experimental tendency to choose more attractive partners, while still explaining observed intra-couple attractiveness correlation and the difference in correlation between casual and serious daters.\n
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\n\n\n\n\n \n \n 2009\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Why is ethnocentrism more common than humanitarianism.\n \n \n \n\n\n \n Shultz, T. R; Hartshorn, M.; and Kaznatcheev, A.\n\n\n \n\n\n\n In Proceedings of the 31st annual conference of the cognitive science society, pages 2100–2105, 2009. Austin, TX: Cognitive Science Society\n \n\n\n\n
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\n\n\n@inproceedings{shultz2009ethnocentrism,\r\n title={Why is ethnocentrism more common than humanitarianism},\r\n author={Shultz, Thomas R and Hartshorn, Max and Kaznatcheev, Artem},\r\n booktitle={Proceedings of the 31st annual conference of the cognitive science society},\r\n pages={2100--2105},\r\n year={2009},\r\n organization={Austin, TX: Cognitive Science Society},\r\n abstract={A compelling agent-based computer simulation suggests that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution (Hammond and Axelrod, 2006). From a neutral start, ethnocentric strategies evolve to dominate other possible strategies (selfish, traitorous, and humanitarian) that differentiate patterns of cooperation with in-group and outgroup agents. We present new analyses and simulations to clarify and explain this outcome by formulating and testing two hypotheses for explaining how ethnocentrism eventually dominates its closest competitor, humanitarianism. Results indicate support for the direct hypothesis that ethnocentrics exploit humanitarian cooperation along the frontiers between ethnocentric and humanitarian groups as world population saturates. We find very little support for the contrasting freerider-suppression hypothesis that ethnocentrics are better than humanitarians at suppressing non-cooperating free riders, although both hypotheses correctly predict a close temporal relation between population saturation and ethnocentric dominance.}\r\n}\r\n\r\n\n\n A compelling agent-based computer simulation suggests that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution (Hammond and Axelrod, 2006). From a neutral start, ethnocentric strategies evolve to dominate other possible strategies (selfish, traitorous, and humanitarian) that differentiate patterns of cooperation with in-group and outgroup agents. We present new analyses and simulations to clarify and explain this outcome by formulating and testing two hypotheses for explaining how ethnocentrism eventually dominates its closest competitor, humanitarianism. Results indicate support for the direct hypothesis that ethnocentrics exploit humanitarian cooperation along the frontiers between ethnocentric and humanitarian groups as world population saturates. We find very little support for the contrasting freerider-suppression hypothesis that ethnocentrics are better than humanitarians at suppressing non-cooperating free riders, although both hypotheses correctly predict a close temporal relation between population saturation and ethnocentric dominance.\n
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