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

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

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

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

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

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

Comparing behavior under risk and under ambiguity in a lifecycle experiment

Experiments on intertemporal consumption typically show that people have difficulties in optimally solving such problems. Previous studies have focused on contexts in which agents are faced with risky future incomes and have to plan over long horizons. We present an experiment comparing decision making under certainty, risk, and ambiguity, over a shorter lifecycle. Results show that behavior in the ambiguity treatment is markedly different than in the risk condition and it is characterized by a significant pattern of under-consumption