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on Evolutionary Economics |
| By: | Christina Matzke, Benedikt Wirth |
| Abstract: | In our model, individual consumers follow simple behavioral decision rules based on imitation and habit as suggested in consumer research, social learning, and related fields. Demand can be viewed as the outcome of a population game whose revision protocol is determined by the consumers' behavioral rules. The consumer dynamics are then analyzed in order to explore the demand side and first implications for a strategic supply side. |
| Keywords: | bounded rationality, social learning, population game, mean dynamic |
| JEL: | C61 C62 C79 |
| URL: | http://d.repec.org/n?u=RePEc:bon:bonedp:bgse10_2008&r=evo |
| By: | Stefania Ottone (EconomEtica and University of Eastern Piedmont; EconomEtica and University of Eastern Piedmont); Ferruccio Ponzano (University of Eastern Piedmont; University of Eastern Piedmont); Luca Zarri (Università di Verona; Dipartimento di Scienze economiche (Università di Verona)) |
| Abstract: | Social norms are ubiquitous in human life. Their role is essential in allowing cooperation to prevail, despite the presence of incentives to free ride. As far as norm enforcement devices are concerned, it would be impossible to have widespread social norms if second parties only enforced them. However, both the quantitative relevance and the motivations underlying altruistic punishment on the part of ‘unaffected’ third parties are still largely unexplored. This paper contributes to shed light on the issue, by means of an experimental design consisting of three treatments: a Dictator Game Treatment, a Third-Party Punishment Game Treatment (Fehr and Fischbacher, 2004) and a Metanorm Treatment, that is a variant of the Third-party Punishment Game where the Recipient can punish the third party. We find that third parties are willing to punish dictators (Fehr and Fischbacher, 2004; Ottone, 2008) and, in doing so, they are affected by ‘reference-dependent fairness’, rather than by the ‘egalitarian distribution norm’. By eliciting players’ normative expectations, it turns out that all of them expect a Dictator to transfer something – not half of the endowment. Consequently, the Observers’ levels of punishment are sensitive to their subjective sense of fairness. A positive relation between the level of punishment and the degree of negative subjective unfairness emerges. Subjective unfairness also affects Dictators’ behaviour: their actual transfers and their ideal transfer are not significantly different. Finally, we interestingly find that third parties are also sensitive to the receivers’ (credible) threat to punish them: as the Dictator’s transfer becomes lower and lower than the Observer’s ideal transfer, the Observer’s reaction is – other things being equal – significantly stronger in the Metanorm Treatment than in the Third-Party Punishment Game Treatment. Hence, despite their being to some extent genuinely nonstrategically motivated, also third parties – like second parties – are sensitive to the costs of punishing. |
| Keywords: | Third-Party Punishment; Moral Sentiments; Material Interests; Subjective Unfairness; Social Norms. |
| JEL: | C72 C9 D63 Z13 |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:ver:wpaper:48&r=evo |
| By: | Daron Acemoglu; Munther A. Dahleh; Ilan Lobel; Asuman Ozdaglar |
| Abstract: | We study the perfect Bayesian equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). The special case where each individual observes all past actions has been widely studied in the literature. We characterize pure-strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, individuals converge (in probability) to taking the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of "expansion in observations". Our main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning. This theorem therefore establishes that, with unbounded private beliefs, there will be asymptotic learning an almost all reasonable social networks. We also show that for most network topologies, when private beliefs are bounded, there will not be asymptotic learning. In addition, in contrast to the special case where all past actions are observed, asymptotic learning is possible even with bounded beliefs in certain stochastic network topologies. |
| JEL: | C72 D83 |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:14040&r=evo |
| By: | Joerg Oechssler (University of Heidelberg, Department of Economics); Andreas Roider (University of Heidelberg, Department of Economics); Patrick W. Schmitz (University of Cologne, Department of Economics) |
| Abstract: | We use a simple, three-item test for cognitive abilities to investigate whether es- tablished behavioral biases that play a prominent role in behavioral economics and finance are related to cognitive abilities. We find that higher test scores on the Cognitive Reflection Test of Frederick (2005) indeed are correlated with lower incidences of the conjunction fallacy, conservatism in updating probabil- ities, and overconfidence. Test scores are also significantly related to subjects' time and risk preferences. We find no influence on anchoring. However, even if biases are lower for people with higher cognitive abilities, they still remain substantial. |
| Keywords: | behavioral finance, biases, cognitive abilities, cognitive reflection test. |
| JEL: | C91 D80 D90 J24 |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:awi:wpaper:0465&r=evo |