Long-Run Selection and the Work Ethic

That individuals contribute in social dilemma interactions even when contributing is costly is a well-established observation in the experimental literature. Since a contributor is always strictly worse off than a non-contributor the question is raised if an intrinsic motivation to contribute can survive in an evolutionary setting. Using recent results on deterministic approximation of stochastic evolutionary dynamics we give conditions for equilibria with a positive number of contributors to be selected in the long run.work ethic, evolution, group selection, public goods, stochastic dynamics

Interviews and adverse selection

Interviewing in professional labor markets is a costly process for firms. Moreover, poor screening can have a persistent negative impact on firms’ bottom lines and candidates’ careers. In a simple dynamic model where firms can pay a cost to interview applicants who have private information about their own ability, potentially large inefficiencies arise from information-based unemployment, where able workers are rejected by firms because of their lack of offers in previous interviews. This effect may make the market less efficient than random matching. We show that the first best can be achieved using either a mechanism with transfers or one without transfers.Decentralized Labor Markets, Professional Labor Markets, Asymmetric Information, Interview costs, Matching

Long-run selection and the work ethic

That individuals contribute in social dilemma interactions even when contributing is costly is a well-established observation in the experimental literature. Since a contributor is always strictly worse off than a non-contributor the question is raised if an intrinsic motivation to contribute can survive in an evolutionary setting. Using recent results on deterministic approximation of stochastic evolutionary dynamics we give conditions for equilibria with a positive number of contributors to be selected in the long run.Work ethic, evolution, group selection, public goods, stochastic dynamics

Stochastic adaption in finite games played by heterogeneous populations

In this paper, I analyze stochastic adaptation in finite n-player games played by heterogeneous populations of myopic best repliers, better repliers and imitators. In each period, one individual from each of n populations, one for each player role, is drawn to play and chooses a pure strategy according to her personal learning rule after observing a sample from a finite history. With a small probability individuals also make a mistake and play a pure strategy at random. I prove that, for a sufficiently low ratio between the sample and history size, only pure-strategy profiles in certain minimal closed sets under better replies will be played with positive probability in the limit, as the probability of mistakes tends to zero. If, in addition, the strategy profiles in one such set have strictly higher payoffs than all other strategy profiles and the sample size is sufficiently large, then the strategies in this set will be played with probability one in the limit. Applied to 2x2 Coordination Games, the Pareto dominant equilibrium is selected for a sufficiently large sample size, but in all symmetric and many asymmetric games, the risk dominant equilibrium is selected for a sufficiently small sample size

A numerical analysis of the evolutionary stability of learning rules

In this paper I define an evolutionary stability criterion for learning rules. Using Monte Carlo simulations, I then apply this criterion to a class of learning rules that can be represented by Camerer and Ho's (1999) model of learning. This class contains perturbed versions of reinforcement and belief learning as special cases. A large population of individuals with learning rules in this class are repeatedly rematched for a finite number of periods and play one out of four symmetric two-player games. Belief learning is the only learning rule which is evolutionarily stable in almost all cases, whereas reinforcement learning is unstable in almost all cases. I also find that in certain games, the stability of intermediate learning rules hinges critically on a parameter of the model and the relative payoffs

Stochastic better-supply dynamics in games

In Young (1993, 1998) agents are recurrently matched to play a finite game and almost always play a myopic best reply to a frequency distribution based on a sample from the recent history of play. He proves that in a generic class of finite n-player games, as the mutation rate tends to zero, only strategies in certain minimal sets closed under best replies will be played with positive probability. In this paper we alter Young's behavioral assumption and allow agents to choose not only best replies, but also better replies. The better-reply correspondence maps distributions over the player's own and her opponents' strategies to those pure strategies which gives the player at least the same expected payoff against the distribution of her opponents' strategies. We prove that in finite n-player games, the limiting distribution will put positive probability only on strategies in certain minimal sets closed under better replies. This result is consistent with and extends Ritzberger's and Weibull's (1995) results on the equivalence of asymptotically stable strategy-sets and closed sets under better replies in a deterministic continuous-time model with sign-preserving selection dynamics

A Numerical Analysis of the Evolutionary Stability of Learning Rules

In this paper I define an evolutionary stability criterion for learning rules. Using Monte Carlo simulations, I then apply this criterion to a class of learning rules that can be represented by Camerer and Ho's (1999) model of learning. This class contains perturbed versions of reinforcement and belief learning as special cases. A large population of individuals with learning rules in this class are repeatedly rematched for a finite number of periods and play one out of four symmetric two-player games. Belief learning is the only learning rule which is evolutionarily stable in almost all cases, whereas reinforcement learning is unstable in almost all cases. I also find that in certain games, the stability of intermediate learning rules hinges critically on a parameter of the model and the relative payoffs.Bounded rationality; Evolutionary game theory; Evolutionary Stability; Learning in games; Belief learning; Reinforcement learning.

Stochastic Better-Reply Dynamics in Games

In Young (1993, 1998) agents are recurrently matched to play a finite game and almost always play a myopic best reply to a frequency distribution based on a sample from the recent history of play. He proves that in a generic class of finite n-player games, as the mutation rate tends to zero, only strategies in certain minimal sets closed under best replies will be played with positive probability. In this paper we alter Young's behavioral assumption and allow agents to choose not only best replies, but also better replies. The better-reply correspondence maps distributions over the player's own and her opponents' strategies to those pure strategies which gives the player at least the same expected payoff against the distribution of her opponents' strategies. We prove that in finite n-player games, the limiting distribution will put positive probability only on strategies in certain minimal sets closed under better replies. This result is consistent with and extends Ritzberger's and Weibull's (1995) results on the equivalence of asymptotically stable strategy-sets and closed sets under better replies in a deterministic continuous-time model with sign-preserving selection dynamics.Evolutionary game theory; Markov chain; stochastic stability; better replies

Stochastic Adaptation in Finite Games Played by Heterogeneous Populations

In this paper, I analyze stochastic adaptation in finite n-player games played by heterogeneous populations of myopic best repliers, better repliers and imitators. In each period, one individual from each of n populations, one for each player role, is drawn to play and chooses a pure strategy according to her personal learning rule after observing a sample from a finite history. With a small probability individuals also make a mistake and play a pure strategy at random. I prove that, for a sufficiently low ratio between the sample and history size, only pure-strategy profiles in certain minimal closed sets under better replies will be played with positive probability in the limit, as the probability of mistakes tends to zero. If, in addition, the strategy profiles in one such set have strictly higher payoffs than all other strategy profiles and the sample size is sufficiently large, then the strategies in this set will be played with probability one in the limit. Applied to 2x2 Coordination Games, the Pareto dominant equilibrium is selected for a sufficiently large sample size, but in all symmetric and many asymmetric games, the risk dominant equilibrium is selected for a sufficiently small sample size.Bounded rationality; Evolutionary game theory; Imitation; Better replies; Markov chain; Stochastic stability; Pareto dominance; Risk dominance

Credit ratings and structured finance

The poor performance of credit ratings on structured finance products has prompted investigation into the role of Credit Rating Agencies (CRAs) in designing and marketing these products. We analyze a two-period reputation model where a CRA both designs and rates securities that are sold to different clienteles: unconstrained investors and investors constrained by minimum quality requirements. When quality requirements for constrained investors are higher, rating inflation increases. Rating inflation decreases if the quality of the asset pool is higher. Securities for both types of investors may have inflated ratings. The motivation for pooling assets derives from tailoring to clienteles and from reputational incentives