Public goods and decay in networks

We propose a simple behavioral model to analyze situations where (1) a group of agents repeatedly plays a public goods game within a network structure and (2) each agent only observes the past behavior of her neighbors, but is affected by the decisions of the whole group. The model assumes that agents are imperfect conditional cooperators, that they infer unobserved contributions assuming imperfect conditional cooperation by others, and that they have some degree of bounded rationality. We show that our model approximates quite accurately regularities derived from public goods game experiments

Identity breeds inequality:Evidence from a laboratory experiment on redistribution

Politics is increasingly driven by identity cleavages, which also affect the discussion about inequality and redistribution. Typically, redistribution is meant to reduce inequality, implying that redistribution neither makes the rich richer nor the former poor the new rich. However, if identity affects redistribution, these limits might no longer be binding, and redistribution could further increase existing inequalities (making the rich richer) or reverse the income ordering to favor the once-poor (which can even be inequality increasing if redistribution is strong). In a laboratory experiment, we investigate redistribution via a novel smooth one-dimensional distribution mechanism that also allows for an increase or reversal of inequality. Decision-makers receive information about the recipients' political orientation, nationality, or seat number during the experiment, and we vary the structure and source of income inequality (income is either earned, random, or unfair). We find most choices of the decision-makers involve redistribution, with only 8 % of choices sticking with the status quo. While most redistribution choices reduce inequality, a larger share—(18 %)—increase inequality by making the rich richer, 13 % of choices reduce overall inequality but make the poor the new rich, and 9 % increase inequality by making the poor very rich. Thus, 40 % of decisions are redistributions that are typically unobserved in common redistribution designs. Ingroup favoritism is a strong motive for redistribution in general, and it is the most important motive for redistribution to increase or reverse inequality. Indeed, 85 % of the inequality-increasing or reversing decisions favor the ingroup. Complementary eye-tracking data show that decision-makers’ attention to information about the recipients’ groups and to poor outliers are related to higher levels of redistribution.</p

Measuring player’s behaviour change over time in public goods game

An important issue in public goods game is whether player's behaviour changes over time, and if so, how significant it is. In this game players can be classified into different groups according to the level of their participation in the public good. This problem can be considered as a concept drift problem by asking the amount of change that happens to the clusters of players over a sequence of game rounds. In this study we present a method for measuring changes in clusters with the same items over discrete time points using external clustering validation indices and area under the curve. External clustering indices were originally used to measure the difference between suggested clusters in terms of clustering algorithms and ground truth labels for items provided by experts. Instead of different cluster label comparison, we use these indices to compare between clusters of any two consecutive time points or between the first time point and the remaining time points to measure the difference between clusters through time points. In theory, any external clustering indices can be used to measure changes for any traditional (non-temporal) clustering algorithm, due to the fact that any time point alone is not carrying any temporal information. For the public goods game, our results indicate that the players are changing over time but the change is smooth and relatively constant between any two time points

Indirect Reciprocity and Strategic Reputation Building in an Experimental Helping Game

We study indirect reciprocity and strategic reputation building in an experimental helping game. At any time only half of the subjects can build a reputation. This allows us to study both pure indirect reciprocity that is not contaminated by strategic reputation building and the impact of incentives for strategic reputation building on the helping rate. We find that while pure indirect reciprocity appears to be important, the helping choice seems to be influenced at least as much by strategic considerations. Strategic do better than non-strategic players and non-reciprocal do better than reciprocal players, casting doubt on previously proposed evolutionary explanations for indirect reciprocity

Identifying discrete behavioural types: A re-analysis of public goods game contributions by hierarchical clustering

We propose a framework for identifying discrete behavioural types in experimental data. We re-analyse data from six previous studies of public goods voluntary contributions games. Using hierarchical clustering analysis, we construct a typology of behaviour based on a simi- larity measure between strategies. We identify four types with distinct sterotypical behaviours, which together account for about 90% of participants. Compared to previous approaches, our method produces a classification in which different types are more clearly distinguished in terms of strategic behaviour and the resulting economic implications

Perceptions of people’s dishonesty towards robots

Dishonest behavior is an issue in human-human interactions and the same might happen in human-robot interactions. To ascertain people’s perceptions of dishonesty, we asked participants to evaluate five different scenarios where someone was being dishonest towards a human or a robot, but we varied the level of autonomy the robot presented. We asked them how guilty they would feel by being dishonest towards a robot, and why do they think people would be dishonest with robots. We see that, regardless of being a human or the autonomy the robot presented, people always evaluated as being wrong to be dishonest. They reported feeling low guilt with a robot. And they expressed that people will be dishonest mostly because of lack of capabilities in the robot to prevent dishonesty, absence of presence, and a human tendency for dishonesty. These results bring implications for the developments of autonomous robots in the future.info:eu-repo/semantics/acceptedVersio

Honesty Requires Time (and Lack of Justifications)

Recent research suggests that refraining from cheating in tempting situations requires self-control, which indicates that serving self-interest is an automatic tendency. However, evidence also suggests that people cheat to the extent that they can justify their unethical behavior to themselves. To merge these different lines of research, we adopted a dual-system approach that distinguished between the intuitive and deliberative cognitive systems. We suggest that for people to restrict their dishonest behavior, they need to have enough time and no justifications for self-serving unethical behavior. We employed an anonymous die-under-cup task in which participants privately rolled a die and reported the outcome to determine their pay. We manipulated the time available for participants to report their outcome (short vs. ample). The results of two experiments support our prediction, revealing that the dark side of people’s automatic self-serving tendency may be overcome when time to decide is ample and private justifications for dishonesty are not available

Human behavior in Prisoner's Dilemma experiments suppresses network reciprocity

During the last few years, much research has been devoted to strategic interactions on complex networks. In this context, the Prisoner's Dilemma has become a paradigmatic model, and it has been established that imitative evolutionary dynamics lead to very different outcomes depending on the details of the network. We here report that when one takes into account the real behavior of people observed in the experiments, both at the mean-field level and on utterly different networks the observed level of cooperation is the same. We thus show that when human subjects interact in an heterogeneous mix including cooperators, defectors and moody conditional cooperators, the structure of the population does not promote or inhibit cooperation with respect to a well mixed population.Comment: 5 Pages including 4 figures. Submitted for publicatio

Investing in Prevention or Paying for Recovery - Attitudes to Cyber Risk

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Broadly speaking an individual can invest time and effort to avoid becoming victim to a cyber attack and/or they can invest resource in recovering from any attack. We introduce a new game called the pre-vention and recovery game to study this trade-off. We report results from the experimental lab that allow us to categorize different approaches to risk taking. We show that many individuals appear relatively risk loving in that they invest in recovery rather than prevention. We find little difference in behavior between a gain and loss framing