Cooperation Survives and Cheating Pays in a Dynamic Network Structure with Unreliable Reputation

In a networked society like ours, reputation is an indispensable tool to guide decisions about social or economic interactions with individuals otherwise unknown. Usually, information about prospective counterparts is incomplete, often being limited to an average success rate. Uncertainty on reputation is further increased by fraud, which is increasingly becoming a cause of concern. To address these issues, we have designed an experiment based on the Prisoner's Dilemma as a model for social interactions. Participants could spend money to have their observable cooperativeness increased. We find that the aggregate cooperation level is practically unchanged, i.e., global behavior does not seem to be affected by unreliable reputations. However, at the individual level we find two distinct types of behavior, one of reliable subjects and one of cheaters, where the latter artificially fake their reputation in almost every interaction.A. A. gratefully acknowledges financial support by the Swiss National Science Foundation (under grants no. 200020-143224, CR13I1-138032 and P2LAP1-161864) and by the Rectors’ Conference of the Swiss Universities (under grant no. 26058983). All authors acknowledge financial support to carry out the experiments by the Faculty of Business and Economics of the University of Lausanne and the fundamental support by Prof. Rafael Lalive. This work has been supported in part by the European Commission through FET Open RIA 662725 (IBSEN) and by the Ministerio de Economía y Competitividad (Spain) under grant FIS2015-64349-P (VARIANCE)

Payoff-based learning explains the decline in cooperation in public goods games

Economic games such as the public goods game are increasingly being used to measure social behaviours in humans and non-human primates. The results of such games have been used to argue that people are pro-social, and that humans are uniquely altruistic, willingly sacrificing their own welfare in order to benefit others. However, an alternative explanation for the empirical observations is that individuals are mistaken, but learn, during the game, how to improve their personal payoff. We test between these competing hypotheses, by comparing the explanatory power of different behavioural rules, in public goods games, where individuals are given different amounts of information. We find: (i) that individual behaviour is best explained by a learning rule that is trying to maximize personal income; (ii) that conditional cooperation disappears when the consequences of cooperation are made clearer; and (iii) that social preferences, if they exist, are more anti-social than pro-social

Learning in a black box

We study behavior in repeated interactions when agents have no information about the structure of the underlying game and they cannot observe other agents’ actions or payoffs. Theory shows that even when players have no such information, there are simple payoff-based learning rules that lead to Nash equilibrium in many types of games. A key feature of these rules is that subjects search differently depending on whether their payoffs increase, stay constant or decrease. This paper analyzes learning behavior in a laboratory setting and finds strong confirmation for these asymmetric search behaviors in the context of voluntary contribution games. By varying the amount of information we show that these behaviors are also present even when subjects have full information about the game

Spread of Yellow Fever Virus outbreak in Angola and the Democratic Republic Congo 2015-2016: a modelling study

Background: Since late 2015, an epidemic of Yellow fever virus (YFV) has caused over 6,554 suspected cases in Angola and the Democratic Republic of Congo, including 387 deaths. We sought to understand the spatial spread of this YFV outbreak to optimise the use of the limited available vaccine stock. Methods: We jointly analysed datasets describing the epidemic of YFV, vector suitability, human demography and mobility in Central Africa in order to understand and predict the expansion of YFV. We used a standard logistic model to infer the district YFV infection risk over the course of the epidemic in the region. Findings: Early spread of YFV was characterized by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reporting cases after only three months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (0·52, 95% CI: 0·34, 0·66). The further away locations were from Luanda the later the invasion date (0·60, 95% CI: 0·52, 0·66). Districts with higher population densities also featured higher risks of sustained transmission. A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others. If at the start of the epidemic sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. Interpretation: Our findings reveal the contributions of ecological and demographic factors to the ongoing spread of the YFV outbreak and provide estimates for where vaccines may be prioritised, although other constraints (e.g. vaccine supply and delivery) need to be accounted for before such insights may be translated into policy