Daniel Villatoro

Social instruments for robust convention emergence

We present the notion of Social Instruments as mechanisms that facilitate the emergence of conventions from repeated interactions between members of a society. Specifically, we focus on two social instruments: rewiring and observation. Our main goal is to provide agents with tools that allow them to leverage their social network of interactions when effectively addressing coordination and learning problems, paying special attention to dissolving metastable subconventions. Our initial experiments throw some light on how Self-Reinforcing Substructures (SRS) in the network prevent full convergence to society-wide conventions, resulting in reduced convergence rates. The use of an effective composed social instrument, observation + rewiring, allow agents to achieve convergence by eliminating the subconventions that otherwise remained meta-stable.

Topology and Memory Effect on Convention Emergence

Social conventions are useful self-sustaining protocols for groups to coordinate behavior without a centralized entity enforcing coordination. We perform an in-depth study of different network structures, to compare and evaluate the effects of different network topologies on the success and rate of emergence of social conventions. While others have investigated memory for learning algorithms, the effects of memory or history of past activities on the reward received by interacting agents have not been adequately investigated. We propose a reward metric that takes into consideration the past action choices of the interacting agents. The research question to be answered is what effect does the history based reward function and the learning approach have on convergence time to conventions in different topologies. We experimentally investigate the effects of history size, agent population size and neighborhood size the emergence of social conventions.

Norm internalization in artificial societies

Internalization is at study in social-behavioural sciences and moral philosophy since long; of late, the debate was revamped within the rationality approach to the study of cooperation and compliance since internalization is a less costly and more reliable enforcement system than social control. But how does it work? So far, poor attention was paid to the mental underpinnings of internalization. This paper advocates a rich cognitive model of different types, degrees and factors of internalization. In order to check the individual and social effect of internalization, we have adapted an existing agent architecture, EMIL-A, providing it with internalization capabilities, turning it into EMIL-I-A. Experiments have proven satisfactory results with respect to the maintenance of cooperation in a proof-of-concept simulation.

Dynamic sanctioning for robust and cost-efficient norm compliance

As explained by Axelrod in his seminal work An Evolutionary Approach to Norms, punishment is a key mechanism to achieve the necessary social control and to impose social norms in a self-regulated society. In this paper, we distinguish between two enforcing mechanisms. i.e. punishment and sanction, focusing on the specific ways in which they favor the emergence and maintenance of cooperation. The key research question is to find more stable and cheaper mechanisms for norm compliance in hybrid social environments (populated by humans and computational agents). To achieve this task, we have developed a normative agent able to punish and sanction defectors and to dynamically choose the right amount of punishment and sanction to impose on them (Dynamic Adaptation Heuristic). The results obtained through agent-based simulation show us that sanction is more effective and less costly than punishment in the achievement and maintenance of cooperation and it makes the population more resilient to sudden changes than if it were enforced only by mere punishment.

Punish and Voice: Punishment Enhances Cooperation when Combined with Norm-Signalling

Material punishment has been suggested to play a key role in sustaining human cooperation. Experimental findings, however, show that inflicting mere material costs does not always increase cooperation and may even have detrimental effects. Indeed, ethnographic evidence suggests that the most typical punishing strategies in human ecologies (e.g., gossip, derision, blame and criticism) naturally combine normative information with material punishment. Using laboratory experiments with humans, we show that the interaction of norm communication and material punishment leads to higher and more stable cooperation at a lower cost for the group than when used separately. In this work, we argue and provide experimental evidence that successful human cooperation is the outcome of the interaction between instrumental decision-making and the norm psychology humans are provided with. Norm psychology is a cognitive machinery to detect and reason upon norms that is characterized by a salience mechanism devoted to track how much a norm is prominent within a group. We test our hypothesis both in the laboratory and with an agent-based model. The agent-based model incorporates fundamental aspects of norm psychology absent from previous work. The combination of these methods allows us to provide an explanation for the proximate mechanisms behind the observed cooperative behaviour. The consistency between the two sources of data supports our hypothesis that cooperation is a product of norm psychology solicited by norm-signalling and coercive devices.

Emergence of conventions through social learning

Societal norms or conventions help identify one of many appropriate behaviors during an interaction between agents. The offline study of norms is an active research area where one can reason about normative systems and include research on designing and enforcing appropriate norms at specification time. In our work, we consider the problem of the emergence of conventions in a society through distributed adaptation by agents from their online experiences at run time. The agents are connected to each other within a fixed network topology and interact over time only with their neighbours in the network. Agents recognize a social situation involving two agents that must choose one available action from multiple ones. No default behavior is specified. We study the emergence of system-wide conventions via the process of social learning where an agent learns to choose one of several available behaviors by interacting repeatedly with randomly chosen neighbors without considering the identity of the interacting agent in any particular interaction. While multiagent learning literature has primarily focused on developing learning mechanisms that produce desired behavior when two agents repeatedly interact with each other, relatively little work exists in understanding and characterizing the dynamics and emergence of conventions through social learning. We experimentally show that social learning always produces conventions for random, fully connected and ring networks and study the effect of population size, number of behavior options, different learning algorithms for behavior adoption, and influence of fixed agents on the speed of convention emergence. We also observe and explain the formation of stable, distinct subconventions and hence the lack of emergence of a global convention when agents are connected in a scale-free network.

Categorizing social norms in a simulated resource gathering society

Our main interest research is focused on reaching a decentralized form of social order through the usage of social norms in virtual communities. In this paper, we analyze the effects of different sets of social norms within a society. The simulation scenario used for the experiments is a metaphor of a resource-gatherer prehistoric society. Finally, we obtain a qualitative ranking of all the possible sets of social norms in our scenario performing agent-based simulation.

The TweetBeat of the City: Microblogging Used for Discovering Behavioural Patterns during the MWC2012

Twitter messages can be located in a city and take the pulse of the citizens’ activity. The temporal and spatial location of spots of high activity, the mobility patterns and the existence of unforeseen bursts constitute a certain Urban Chronotype, which is altered when a city-wide event happens, such as a world-class Congress. This paper proposes a Social Sensing Platform to track the Urban Chronotype, able to collect the Tweets, categorize their provenance and extract knowledge about them. The clustering algorithm DBScan is proposed to detect the hot spots, and a day to day analysis reveals the movement patterns. Having analyzed the Tweetbeat of Barcelona during the 2012 Mobile World Congress, results show that a easy-to-deploy social sensor based on Twitter is capable of representing the presence and interests of the attendees in the city and enables future practical applications. Initial empirical results haven shown a significant alteration in the behavioural patterns of users and clusters of activity within the city.

The Norm-Signaling Effects of Group Punishment: Combining Agent-Based Simulation and Laboratory Experiments

Punishment plays a crucial role in favoring and maintaining social order. Recent studies emphasize the effect of the norm-signaling function of punishment. However, very little attention has been paid so far to the potential of group punishment. We claim that when inflicted by an entire group, the recipient of punishment views it as expressing norms. The experiments performed in this work provide evidence that humans are motivated not only by material incentives that punishment imposes but also by normative information that it conveys. The same material incentive has a different effect on the individuals’ future compliance depending on the way it is implemented, having a stronger effect when it also conveys normative information. We put forward the hypothesis that by inflicting equal material incentives, group punishment is more effective in enhancing compliance than uncoordinated punishment, because it takes advantage of the norm-signaling function of punishment. In support of our hypothesis, we present cross-methodological data, that is, data obtained through agent-based simulation and laboratory experiments with human subjects. The combination of these two methods allows us to provide an explanation for the proximate mechanisms generating the cooperative behavior observed in the laboratory experiment.

Exploring The Dimensions Of Convention Emergence In Multiagent Systems

Social conventions are useful self-sustaining protocols for groups to coordinate behavior without a centralized entity enforcing coordination. The emergence of such conventions in different multi agent network topologies has been investigated by several researchers, although exploring only specific cases of the convention emergence process. In this work we will provide multi-dimensional analysis of several factors that we believe determines the process of convention emergence, such as: the size of agents memory, the population size and structure, the learning approach taken by agents, the amount of players in the interactions, or the convention search space dimension. Although we will perform an exhaustive study of different network structures, we are concerned that different topologies will affect the emergence in different ways. Therefore, the main research question in this work is comparing and studying effects of different topologies on the emergence of social conventions. While others have investigated memory for learning algorithms, the effects of memory on the reward have not been investigated thoroughly. We propose a reward metric that is derived directly from the history of the interacting agents. Another research question to be answered is what effect does the history based reward function and the learning approach have on convergence time in different topologies. Experimental results show that all the factors analyzed affect differently the convention emergence process, being such information very useful for policy-makers when designing self-regulated systems.