# Termites in the trading system summary

Evolutionary game theory EGT is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and analytics into which Darwinian competition can be modelled.

It originated in with John Maynard Smith and George R. Price 's formalisation of contests, analysed as strategies, and the mathematical criteria that can be used to predict the results of competing strategies. Evolutionary game theory differs from classical game theory in focusing more on the dynamics of strategy change.

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This is influenced by the frequency of the competing strategies in the population. Evolutionary game theory has helped to explain the basis of altruistic behaviours in Darwinian evolution. It has in turn become of interest to economistssociologistsanthropologistsand philosophers.

Classical non-cooperative game theory was conceived by John von Neumann to determine optimal strategies in competitions between adversaries. A contest involves players, all of whom have a choice of moves. Games can be a single round or repetitive. The approach a player takes in making his moves constitutes his strategy.

Rules govern the outcome for the moves taken by the players, and outcomes produce payoffs for the players; rules and resulting payoffs can be expressed as decision trees or in a payoff matrix. Classical theory requires the players to make rational choices. Each player must consider the strategic analysis that his opponents are making to make his own choice of moves.

Evolutionary game theory started with the problem of how to explain ritualized animal behaviour in a conflict situation; "why are animals so 'gentlemanly or ladylike' in contests for resources? John Maynard Smith considered that incompatible with Darwinian thought, [5] where selection occurs at an individual level, so self-interest is rewarded while seeking the common good is not.

Maynard Smith, a mathematical biologist, turned to game theory as suggested by George Price, though Richard Lewontin 's attempts to use the theory had failed. Maynard Smith realised that an evolutionary version of game theory does not require players to act rationally — only that they have a strategy.

The results of a game show how good that strategy was, just as evolution tests alternative strategies for the ability to survive and reproduce. In biology, strategies are genetically inherited traits that control an individual's action, analogous with computer programs. The success of a strategy is determined by how good the strategy is in the presence of competing strategies including itselfand of the frequency with which those strategies are used. Participants aim to produce as many replicas of themselves as they can, and the payoff is in units of fitness relative worth in being able to reproduce.

It is always a multi-player game with many competitors. Rules include replicator dynamics, in other words how the fitter players will spawn more replicas of themselves into the population and how the less fit will be culled, in a replicator equation.

The replicator dynamics models heredity but not mutation, and assumes asexual reproduction for the sake of simplicity. Games are run repetitively with no terminating conditions. Results include the dynamics of changes in the population, the success of strategies, and any equilibrium states reached. Unlike in classical game theory, players do not choose their strategy and cannot change it: EGT encompasses Darwinian evolution, including competition the gamenatural selection replicator dynamicsand heredity.

EGT has contributed to the understanding of group selectionsexual selectionaltruismparental careco-evolutionand ecological dynamics.

Many counter-intuitive situations in these areas have been put on a firm mathematical footing by the use of these models. The common way to study the evolutionary dynamics in games is through replicator equations.

These show the growth rate of the proportion of organisms using a certain strategy and that rate is equal to the difference between the average payoff of that strategy and the average payoff of the population as a whole.

The attractors stable fixed points of the equations are equivalent with evolutionarily stable states. A strategy which can survive all "mutant" strategies is considered evolutionary stable. In the context of animal behavior, this usually means such strategies are programmed and heavily influenced by geneticsthus making any player or organism's strategy determined by these biological factors.

Evolutionary games are mathematical objects with different rules, payoffs, and mathematical behaviours. Each "game" represents different problems that organisms have to deal with, and the strategies they might adopt to survive and reproduce. Evolutionary games are often given colourful names and cover stories which describe the general situation of a particular game. Representative games include hawk-dove[1] war of attrition[14] stag huntproducer-scroungertragedy of the commonsand prisoner's dilemma.

Strategies for these games include Hawk, Dove, Bourgeois, Prober, Defector, Assessor, and Retaliator. The various strategies compete under the particular game's rules, and the mathematics are used to determine the results and behaviours. The first game that Maynard Smith analysed is the classic Hawk Dove [a] game. It was conceived to analyse Lorenz and Tinbergen's problem, a contest over a shareable resource. The contestants can be either Hawk or Dove. These are two subtypes or morphs of one species with different strategies.

The Hawk first displays aggression, then escalates into a fight until it either wins or is injured loses. The Dove first displays aggression, but if faced with major escalation runs for safety.

If not faced with such escalation, the Dove attempts to share the resource. Given that the resource is given the value V, the damage from losing a fight is given cost C: The actual payoff however depends on the probability of meeting a Hawk or Dove, which in turn is a representation of the percentage of Hawks and Doves in the population when a particular contest takes place. That in turn is determined by the results of all of the previous contests. The population regresses to this equilibrium point if any new Hawks or Doves make a temporary perturbation in the population.

The solution of the Hawk Dove Game explains why most animal contests involve only ritual fighting behaviours in contests rather than outright battles. The result does not at all depend on good of the species behaviours as suggested by Lorenz, but solely on the implication of actions of so-called selfish genes. In the Hawk Dove game the resource is shareable, which gives payoffs to both Doves meeting in a pairwise contest. Where the resource is not shareable, but an alternative resource might be available by backing off and trying elsewhere, pure Hawk or Dove strategies are less effective.

If an unshareable resource is combined with a high cost of losing a contest injury or possible death both Hawk and Dove payoffs are further diminished. A safer strategy of lower cost display, bluffing and waiting to win, is then viable — a Bluffer strategy.

The game then becomes one of accumulating costs, either the costs of displaying or the costs of prolonged unresolved engagement. It is effectively an auction; the winner is the contestant who will swallow the greater cost while the loser gets the same cost as the winner but no resource. This is because in the war of attrition any strategy that is unwavering and predictable is unstable, because it will ultimately be displaced by a mutant strategy which relies on the fact that it can best the existing predictable strategy by investing an extra small delta of waiting resource to ensure that it wins.

Therefore, only a random unpredictable strategy can maintain itself in a population of Bluffers. The contestants in effect choose an acceptable cost to be incurred related to the value of the resource being sought, effectively making a random bid as part of a mixed strategy a strategy where a contestant has several, or even many, possible actions in his strategy.

This implements a distribution of bids for a resource of specific value V, where the bid for any specific contest is chosen at random from that distribution. The distribution an ESS can be computed using the Bishop-Cannings theorem, which holds true for any mixed strategy ESS.

The result is that the cumulative population of quitters for any particular cost m in this "mixed strategy" solution is:. The intuitive sense that greater values of resource sought leads to greater waiting times is borne out.

This is observed in nature, as in male dung flies contesting for mating sites, where the timing of disengagement in contests is as predicted by evolutionary theory mathematics. In the War of Attrition there must be nothing that signals the size of a bid to an opponent, otherwise the opponent can use the cue in an effective counter-strategy. There is however a mutant strategy which can better a Bluffer in the War of Attrition Game if a suitable asymmetry exists, the Bourgeois strategy.

Bourgeois uses an asymmetry of some sort to break the deadlock. In nature one such asymmetry is possession of a resource. The strategy is to play a Hawk if in possession of the resource, but to display then retreat if not in possession. This requires greater cognitive capability than Hawk, but Bourgeois is common in many animal contests, such as in contests among mantis shrimps and among speckled wood butterflies. Games like Hawk Dove and War of Attrition represent pure competition between individuals and have no attendant social elements.

Where social influences apply, competitors have four possible alternatives for strategic interaction. This is shown on the adjacent figure, where a plus sign represents a benefit and a minus sign represents a cost. At first glance it may appear that the contestants of evolutionary games are the individuals present in each generation who directly participate in the game.

But individuals live only through one game cycle, and instead it is the strategies that really contest with one another over the duration of these many-generation games. So it is ultimately genes that play out a full contest — selfish genes of strategy.

The contesting genes are present in an individual and to a degree in all of the individual's kin. This can sometimes profoundly affect which strategies survive, especially with issues of cooperation and defection. William Hamilton[20] known for his theory of kin selectionexplored many of these cases using game theoretic models. For such games Hamilton defined an extended form of fitness — inclusive fitnesswhich includes an individual's offspring as well as any offspring equivalents found in kin.

Now if individual a i sacrifices his "own average equivalent fitness of 1" by accepting a fitness cost C, and then to "get that loss back", w i must still be 1 or greater than Hamilton went beyond kin relatedness to work with Robert Axelrodanalysing games of co-operation under conditions not involving kin where reciprocal altruism comes into play. Eusocial insect workers forfeit reproductive forex darmowy bonus to their queen.

It has been suggested that Kin Selection, based on the genetic makeup of these workers, may predispose them to altruistic behaviour.

### Termites in the Trading System - Jagdish Bhagwati - Oxford University Press

This explanation of insect eusociality has however been challenged by a few highly noted evolutionary game theorists Nowak and Wilson [26] who have published a controversial alternative game termites in the trading system summary explanation based on a sequential development and group selection effects proposed for these insect species.

A difficulty of the theory of evolution, recognised by Darwin himself, was the problem of altruism. If the basis for selection is at individual level, altruism makes no sense at all. But universal selection at the group level for the good of the species, not the individual fails to pass the test of the mathematics of game theory and is certainly not the general case in nature.

The solution to this paradox can be putty command line save session in the application of evolutionary game theory to the prisoner's dilemma game — a game which tests the payoffs of cooperating or in defecting from cooperation.

It is certainly the most studied game in all of game theory. The analysis of prisoner's dilemma is as a repetitive game.

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This affords competitors the possibility of retaliating for defection in previous rounds of the game. Many strategies have been tested; the best competitive strategies are general cooperation with a reserved retaliatory response if necessary.

The pay-off for any single round converting options to restricted stock the game is defined by the pay-off matrix for a single round game shown in bar chart 1 below.

In multi-round games the different choices — Co-operate or Defect — can be made in any particular round, resulting in a certain round highlight input value jquery. It is, however, the possible accumulated pay-offs over the multiple rounds that count in shaping the overall pay-offs for differing multi-round strategies such as Tit-for-Tat.

The straightforward single round prisoner's dilemma game. The classic prisoner's dilemma game payoffs gives a player a maximum payoff if he defect and his partner co-operates this choice is known as temptation.

If however the player co-operates and his partner defects, he gets the worst possible result the suckers payoff. In these payoff conditions the best choice a Nash equilibrium is to defect.

Prisoner's dilemma played repeatedly. The strategy what is a foreign exchange trader is Tit-for-Tat which alters basics of stock markets pdf based on the action taken by a partner in the previous round — i.

The effect of this strategy in accumulated payoff over many rounds is to produce a higher payoff for both players co-operation and a lower payoff for defection. This removes the Temptation to defect.

The suckers payoff also becomes less, although "invasion" by a pure defection strategy is not entirely eliminated. Altruism takes place when one buying overseas shares directly, at a cost C to itself, exercises a strategy that provides a benefit B to another individual. The cost may consist of a loss of capability or resource which helps in the battle for survival and reproduction, or an added risk to its own survival.

Do put options adjust for dividends affect strategies can arise through:. It has been argued that human behaviours in establishing moral system as well as the expending of significant energies in human society for tracking individual reputation is a direct effect of societies reliance on strategies of indirect reciprocation. Organisms that use social score are termed Discriminators, and require a higher level of cognition than strategies of simple direct reciprocity.

As evolutionary biologist David Haig put it — "For direct reciprocity termites in the trading system summary need a face; for indirect reciprocity you need a name".

The evolutionarily stable strategy ESS is akin to Nash equilibrium in classical game theory, but with mathematically premier trade workshop forex james dix criteria. Nash Equilibrium is a game equilibrium where it is not rational for any player to deviate from their present strategy. An ESS here a state of game dynamics where, in a very large population of competitors, another mutant strategy cannot successfully enter the population to disturb the existing dynamic which itself depends on the population mix.

Therefore, a successful strategy with an ESS must be both effective against competitors when it is rare — to enter the previous competing population, and successful when later in high proportion in the population — to defend itself. This in compensation strategy binary options means that the strategy must be successful when it contends with others exactly like itself.

The ESS state can be solved for by exploring either the dynamics of population change to determine an ESS, or by solving equations for the stable stationary point conditions which define an ESS. Similarly, using inequalities, it can be shown that an additional Hawk or Dove mutant entering this ESS state eventually results in less fitness for their kind — both a true Nash and an ESS equilibrium. This example shows that when the risks of contest injury or death the Cost C is significantly greater than the potential reward the benefit value Vthe stable population will be mixed between aggressors and doves, and the proportion of doves will exceed that of the aggressors.

This explains behaviours observed in nature. An evolutionary game pak rupees forex rates turns out to be a children's game is rock-paper-scissors. The game is simple — rock beats scissors blunts itscissors beats paper cuts itand paper beats rock wraps it up. Anyone who has ever played this simple game knows that it is not sensible to have any favoured play — the opponent will soon notice this and switch to the winning counter-play.

The best strategy a Nash equilibrium is to play a mixed random game with any of the three plays taken a third of earn money from survey in india time.

This, in EGT terms, is a mixed strategy. But many lifeforms are incapable of mixed behavior — they only exhibit one strategy known as a pure strategy. If the game currency rate euro to aed played only with the pure Rock, Paper and Scissors strategies the evolutionary game is dynamically unstable: Rock mutants can enter an all scissor population, but then — Paper mutants can take over an all Rock population, but then — Scissor mutants can take over an cotton stock market symbol for gold futures Paper population — and on and on This is easily seen on the game payoff matrix, where if the paths of mutant invasion are noted, it can be seen that the mutant "invasion paths" form into a loop.

This in triggers a cyclic invasion pattern. Rock-paper-scissors incorporated into an evolutionary game has been used for modelling natural processes in the study of ecology. The social cyclic behaviors, predicted stock broker sand lake evolutionary game theory, have been observed in various laboratory experiments. The side-blotched lizard Uta stansburiana is polymorphic with three morphs [44] that each pursues a different mating strategy.

However the blue throats cannot overcome the trade stock automatisation aggressive orange throats. The overall situation corresponds to the Rock, Scissors, Paper game, creating a six-year population cycle. When he read that these lizards were essentially engaged in a game with rock-paper-scissors structure, John Maynard Smith is said to have exclaimed "They have read my book!

Aside from the difficulty of explaining how altruism exists in many evolved organisms, Darwin kinh doanh tien te forex also bothered by a second conundrum — why do a significant number of species have phenotypical attributes that are patently disadvantageous to them with respect to their survival — and should by the process of natural section be selected against — e.

Regarding this issue Darwin wrote to a colleague "The sight of a feather in a peacock's tail, whenever I gaze at it, makes me sick. On analysis, problems of biological life are not at all unlike the problems that define economics — eating akin to resource acquisition and managementsurvival competitive strategy and reproduction investment, risk and return. Game theory was originally conceived as a mathematical analysis of economic processes and indeed this is why it has proven so useful in explaining so many biological behaviours.

One important further refinement of the EGT model that has economic overtones rests on the analysis of COSTS. A simple model of cost assumes that all competitors suffer the same penalty imposed by the Game costs, but this is not the case. More successful players will be endowed with or will have accumulated a higher "wealth reserve" or "affordability" than less successful players.

This wealth effect in evolutionary game theory is represented mathematically by " resource holding potential RHP " and shows that the effective cost to a competitor with higher RHP are not as great as for a competitor with a lower RHP. As a higher RHP individual is more desirable mate in producing potentially successful offspring, it is only logical that with sexual selection RHP should have evolved to be major currency pairs forex in some way by the competing rivals, and for this to work this signalling must be done honestly.

Amotz Zahavi has developed this thinking in what is known as the handicap principle[47] where superior competitors signal their superiority by a costly display.

As higher RHP individuals can properly afford such a costly display this signalling is inherently honest, and can be taken as such by the signal receiver. Nowhere in nature is this better illustrated than in the magnificent and costly plumage of the peacock. The mathematical proof of the handicap principle was developed by Alan Grafen using evolutionary game-theoretic modelling.

A third, co-evolutionarydynamic combines intra-specific and inter-specific competition. Examples include predator-prey competition and host-parasite co-evolution, as well as mutualism. Evolutionary game models have been created for pairwise and multi-species coevolutionary systems. In competitive non-mutualistic inter-species coevolutionary system the species are involved in an arms race — where adaptations that are better at competing against the other species tend to be preserved.

Both game payoffs and replicator dynamics reflect this. This leads to a Red Queen dynamic where the protagonists must "run as fast as they can to just stay in one place".

A number of EGT models have been produced to encompass coevolutionary situations. A key factor applicable in these coevolutionary systems is the continuous adaptation of strategy in such arms races. Coevolutionary modelling therefore often includes genetic algorithms to reflect mutational effects, while computers simulate the dynamics of the overall coevolutionary game. The resulting dynamics are studied as various parameters are modified. Because several variables are simultaneously at play, solutions become the province of multi-variable optimisation.

The mathematical criteria of determining stable points are Pareto efficiency and Pareto dominance, a measure of solution optimality peaks in multivariable systems. Carl Bergstrom and Michael Lachmann apply evolutionary game theory to the division of benefits in mutualistic interactions between organisms.

Darwinian assumptions about fitness are modeled using replicator dynamics to show that the organism evolving at a slower rate in a mutualistic relationship gains a disproportionately high share of the benefits or payoffs.

With this understanding in place it is then appropriate to see if other, more subtle, parameters second order effects further impact the primary behaviours or shape additional behaviours in the system. Some of these key extensions to EGC are:. Alternatively, agents might have access to an arbitrary signal initially uncorrelated to strategy but becomes correlated due to evolutionary dynamics.

This is the green-beard effect or evolution of ethnocentrism in humans. From molecular to multicellular level, a signaling game model with information asymmetry between sender and receiver might be appropriate, such as in mate attraction [48] or evolution of translation machinery from RNA strings. From Wikipedia, the free encyclopedia. War of attrition game. Dung Fly Scatophaga stercoraria — a War of Attrition player.

The mantis shrimp guarding its home with the Bourgeois Strategy. Competitive Co-evolution - The rough-skinned newt Tarricha granulosa is highly toxic, due to an evolutionary arms race with a predator, the common garter snake Thamnophis sirtaliswhich in turn is highly tolerant of the poison. The two are locked in a Red Queen arms race. Mutualistic Coevolution - Darwin's orchid Angraecum sesquipedale and the moth Morgan's sphinx Xanthopan morgani have a mutual relationship where the moth gains pollen and the flower is pollinated.

Networks, Crowds, and Markets: Reasoning About a Highly Connected World PDF. Toward a History of Game Theory. A Reason for Everything. Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics. Evolution and the Theory of Games. Game Theory and Animal Behavior. Game theory and evolutionary biology. Handbook of Game Theory with Economic Applications, Volume 2. Evolutionary games and population dynamics. The Price of Altruism. Principles of Animal Behavior.

Annual Review of Ecology and Systematics. Evolution and the Levels of Selection. Proceeding of the Royal Society. The Evolution of Cooperation. Chapters 1 to 4. The Biology of Moral Systems. Journal of Theoretical Biology. Evolutionarily Stable Strategies with Two Types of Players J. Evolutionarily Stable Strategies and Game Dynamics Math. A Continuous Time Experiment". Review of Economic Studies.

Mate choice games, context-dependent good genes, and genetic cycles in the side-blotched lizard, Uta stansburiana. Measurements of adaptive progress in co-evolutionary simulations", European Conference on Artificial Life, p. Evolutionary Games in Wireless Networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B 40 3: Robustness of ethnocentrism to changes in inter-personal interactions. In Complex Adaptive Systems—AAAI Fall Symposium.

Journal of the Royal Society Interface. Uses authors parameter link. Hardy-Weinberg law Genetic linkage Identity by descent Linkage disequilibrium Fisher's fundamental theorem Neutral theory Shifting balance theory Price equation Coefficient of relationship Fitness Heritability. Natural Sexual Artificial Ecological. Effects of selection on genomic variation. Genetic hitchhiking Background selection.

Small population size Population bottleneck Founder effect Coalescence Balding—Nichols model. Evolution Microevolution Evolutionary game theory Fitness landscape Genetic genealogy Quantitative genetics. Index of evolutionary biology articles. Topics in game theory. Normal-form game Extensive-form game Escalation of commitment Graphical game Cooperative game Succinct game Information set Hierarchy of beliefs Preference.

Nash equilibrium Subgame perfection Mertens-stable equilibrium Bayesian Nash equilibrium Perfect Bayesian equilibrium Trembling hand Proper equilibrium Epsilon-equilibrium Correlated equilibrium Sequential equilibrium Quasi-perfect equilibrium Evolutionarily stable strategy Risk dominance Core Shapley value Pareto efficiency Gibbs equilibrium Quantal response equilibrium Self-confirming equilibrium Strong Nash equilibrium Markov perfect equilibrium.

Dominant strategies Pure strategy Mixed strategy Tit for tat Grim trigger Collusion Backward induction Forward induction Markov strategy. Symmetric game Perfect information Simultaneous game Sequential game Repeated game Signaling game Screening game Cheap talk Zero-sum game Mechanism design Bargaining problem Stochastic game n -player game Large Poisson game Nontransitive game Global game Strictly determined game Potential game.

Minimax theorem Nash's theorem Purification theorem Folk theorem Revelation principle Arrow's impossibility theorem. Tucker Amos Tversky Ariel Rubinstein Daniel Kahneman David K. Gillies Drew Fudenberg Eric Maskin Harold W. Flood Oskar Morgenstern Paul Milgrom Peyton Young Reinhard Selten Robert Axelrod Robert Aumann Robert B. Wilson Roger Myerson Samuel Bowles Thomas Schelling William Vickrey.

All-pay auction Alpha—beta pruning Bertrand paradox Bounded rationality Combinatorial game theory Confrontation analysis Coopetition List of game theorists List of games in game theory No-win situation Topological game Tragedy of the commons Tyranny of small decisions. Retrieved from " https: Evolutionary game theory Evolutionary dynamics Game theory Mathematical and quantitative methods economics.

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By using this site, you agree to the Terms of Use and Privacy Policy. Privacy policy About Wikipedia Disclaimers Contact Wikipedia Developers Cookie statement Mobile view. The concept of Kin Selection is that: Evolutionary Game participants are genes of strategy.

The best payoff for an individual is not necessarily the best payoff for the gene. In any generation the player gene is NOT only in one individual, it is in a Kin-Group. The highest fitness payoff for the Kin Group is selected by natural selection. Therefore, strategies that include self-sacrifice on the part of individuals are often game winners — the evolutionarily stable strategy. Animals must live in kin-group during part of the game for the opportunity for this altruistic sacrifice ever to take place.

Games must take into account Inclusive Fitness. Fitness function is the combined fitness of a group of related contestants — each weighted by the degree of relatedness — relative to the total genetic population.

The mathematical analysis of this gene centric view of the game leads to Hamilton's rule, that the relatedness of the altruistic donor must exceed the cost-benefit ratio of the altruistic act itself: A game theoretic embodiment of "I'll scratch your back if you scratch mine".

A pair of individuals exchange favours in a multi-round game. The individuals are recognisable to one another as partnered. The term "direct" applies because the return favour is specifically given back to the pair partner only. The characteristics of the multi-round game produce a danger of defection and the potentially lesser payoffs of cooperation in each round, but any such defection can lead to punishment in a following round — establishing the game as repeated prisoner's dilemma.

Therefore, the family of tit-for-tat strategies come to the fore. Related or non related contestants trade favours but without partnering. A return favour is "implied" but with no specific identified source who is to give it. This behaviour is akin to "I'll scratch your back, you scratch someone else's back, another someone else will scratch mine probably ".

The return favour is not derived from any particular established partner. The potential for indirect reciprocity exists for a specific organism if it lives in a cluster of individuals who can interact over an extended period of time. The game is highly susceptible to defection, as direct retaliation is impossible. Therefore, indirect reciprocity will not work without keeping a social score, a measure of past co-operative behaviour.

The mathematics leads to a modified version of Hamilton's Rule where: Geographic factors in evolution include gene flow and horizontal gene transfer. Spatial game models represent geometry by putting contestants in a lattice of cells: Winning strategies take over these immediate neighbourhoods and then interact with adjacent neighbourhoods.

This model is useful in showing how pockets of co-operators can invade and introduce altruism in the Prisoners Dilemma game, [54] where Tit for Tat TFT is a Nash Equilibrium but NOT also an ESS. Spatial structure is sometimes abstracted into a general network of interactions.

In EGT as in conventional Game Theory the effect of Signalling the acquisition of information is of critical importance, as in Indirect Reciprocity in Prisoners Dilemma where contests between the SAME paired individuals are NOT repetitive.

This models the reality of most normal social interactions which are non-kin related. Unless a probability measure of reputation is available in Prisoners Dilemma only direct reciprocity can be achieved. Many evolutionary games have been modelled in finite populations to see the effect this may have, for example in the success of mixed strategies.

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