Behavioral Economics Commons^™

Imitation In Heterogeneous Populations, Jonas Hedlund, Carlos Oyarzun

Carlos Oyarzun

Response Functions, Carlos Oyarzun, Adam Sanjurjo, Hien Nguyen

Carlos Oyarzun

Convergence In Models With Bounded Expected Relative Hazard Rates, Carlos Oyarzun, Johannes Ruf

Carlos Oyarzun

We provide a general framework to study stochastic sequences related to an array of models in different literatures, including models of individual learning in economics, learning automata in computer sciences, social learning in marketing, and many others. In this setup, we study the asymptotic properties of a class of stochastic sequences that take values in [0,1] and satisfy a property that we call “bounded expected relative hazard rates.” We provide sufficient conditions for related sequences, which, compared to the original sequence, either move slowly or slow down over time, that yield con- vergence to one with high probability or almost …

A Note On Absolutely Expedient Learning Rules, Carlos Oyarzun

Carlos Oyarzun

I provide a full characterization of the set of absolutely expedient learning rules introduced in Börgers, Morales, and Sarin (2004) [“Expedient and monotone learning rules,” Econometrica, 72, 383–405]. The expected change in the expected payoff can be written as a quadratic form on the vector of relative expected payoffs of the strategies. This permits use of standard linear algebra arguments to provide a characterization in terms of the matrix defining this quadratic form.

Learning And Risk Aversion, Carlos Oyarzun, Rajiv Sarin

Carlos Oyarzun

Abstract We study the manner in which learning shapes behavior towards risk when individuals are not assumed to know, or to have beliefs about, probability distributions. In any period, the behavior change induced by learning is assumed to depend on the action chosen and the payoff obtained. We characterize learning processes that, in expected value, increase the probability of choosing the safest (or riskiest) actions and provide sufficient conditions for them to converge, in the long run, to the choices of risk averse (or risk seeking) expected utility maximizers. We provide a learning theoretic motivation for long run risk choices, …

Mean And Variance Responsive Learning, Carlos Oyarzun, Rajiv Sarin

Carlos Oyarzun

A learning rule is variance-averse if the expected reduced-distribution of payoffs in the next period has a smaller variance than that of the current reduced-distribution, in every set where all the actions provide the same expected payoff. A learning rule is monotonically variance-averse if it is expected to add probability to the set of actions that have the smallest variance in the set, when all the actions have the same expected payoff. A learning rule is monotonically mean-variance-averse if it is expected to add probability to the set of actions that have the highest expected payoff and smallest variance whenever …

Monotone Imitation, Carlos Oyarzun, Johannes Ruf

Carlos Oyarzun

We analyze the social learning process of a group of individuals who have limited information about the payoff distributions of each action. We say that a behavioral rule is first-order monotone (FOM) if the number of individuals who play actions with first-order stochastic dominant payoff distributions is expected to increase in any environment. We provide a characterization of FOM rules. Both Imitate if Better and Schlag’s (J Econ Theory 78:130-156, 1998) Proportional Imitation rule are FOM. No FOM rule is dominant in the sense of having the best performance in every environment.