Andrea Scalco

The Pursuit of Happiness: A Model of Group Formation

We developed an agent-based model with the aim of investigating the effect of the interaction among several virtual actors characterized by (1) a certain level of emotional intelligence and (2) an individual behavioral proneness to act positively or negatively within social interactions. The goal of each agent is to achieve a satisfactory internal state, which is consequential to the positive/negative effects derived by the incurred social interactions. As a result, when the simulation run, we observe the spontaneous emergence of groups. Moreover, it could be easily noted that the large majority of the defectors are incapable to join to any group, and the few groups that accept defectors are not able to maintain more than one of this kind of actors. Finally, we studied the ratios between virtual actors when stable configurations are reached.

Exploring Selfish versus Altruistic Behaviors in the Ultimatum Game with an Agent-Based Model

In the present study we developed a simulation where agents play repeatedly the ultimatum game with the aim of exploring their earnings for several thresholds of willingness to accept proposals. At the same time, the scope is also to provide a simple and easily understandable simulation of the ultimatum game. Particularly, the simulation generates two kinds of agents, whose proposals are generated accordingly to their selfish or selfless behavior; subsequently, agents compete in order to increase their wealth playing the ultimatum game with a random-stranger matching. The trend emerged by simulation charts shows how, even when altruistic agents bid higher proposals than those following selfish behaviors, the average mean cash earned is higher for the former agents than the latter. A second fact is that, looking at the system as a whole, altruistic punishment leads to a reduction of the resources exploited by the agents. Finally, we introduced the psychological construct of trait emotional intelligence, briefly discussing the value of its implementation into computational simulations.

Compassion and Prosocial Behavior. Is it Possible to Simulate them Virtually

In the field of artificial intelligence, a question dealing with computer and cognitive science is arising and becoming more and more crucial: Can we design agents so sophisticated that they are capable of mimicking emotional behaviors in general as well as specific emotions like compassion or empathy? Despite the production of different computational models, their integration with cognitive and psychological theories remains a central problem. Reasons are both methodological and theoretical. Primarily, it is difficult to quantify the impact of such factors as individual differences, inclinations and personality traits. In addition, Agent-Based Models (ABMs) often use linear dynamics, even in describing emotions, without considering the basis of psychophysics. Bearing in mind this and focusing on compassion as a particular emotion, the paper aims to present a “Decalogue” for those interested in designing agents capable of mimicking human emotional behaviors. In the paper, compassion will be translated as prosocial behavior.

The Implementation of the Theory of Planned Behavior in an Agent-Based Model for Waste Recycling: A Review and a Proposal

In the near future, the waste management sector is expected to reduce substantially the adverse effects of garbage on the environment. However, the increasing complexity of the current waste management systems makes the optimization of the waste management strategies and policies challenging. For this reason, waste prevention is the most desirable goal to achieve. Despite this, low levels of household recycling represent the key factor that complicates the current scenario. Keeping this in mind, the present work investigates the determinants of recycling behavior through the development of an agent-based model. Particularly, we examined what would induce households to increase the probability to engage in recycling behaviors on the base of the individual attitude and sensitivity to social norms. The Theory of Planned Behavior (TPB) has been implemented as agents’ cognitive model in environmental studies with the aim to predict recycling outcomes. Furthermore, in order to increase the realism of the simulation and the adherence of the model with the theory, we followed two strategies: firstly, we used real data to model a city district (Diong, Internship Report: Integrated Waste Management in Kaohsiung City, 2012). Secondly, we made use of the coefficients of the structural equation model presented in the work by Chu and Chiu (J Appl Soc Psychol 33(3):604–626, 2003) to build the agents’ cognitive model. As a whole, the results are in line with literature on descriptive social norms. Furthermore, the results indicate that the introduction of descriptive social norms represents a valuable strategy for public policies to improve household recycling: however, injunctive social norms are needed first.

Trait Emotional Intelligence: Modelling Individual Emotional Differences in Agent-Based Models

From a psychological perspective, the construct of emotional intelligence (EI) is strictly related to the individual experience of emotions. Despite the several prominent theories emerged during the years [1,2,3], all authors recognize that emotional intelligence concerns two main aspects of individual emotional experience: the faculty to recognize and control own emotions, and the ability to understand and influence what others feel. In this way, EI can be considered as a superior dimension useful to the perception, control and management of emotions: from an individual viewpoint (i.e. what particular emotion am I feeling in this moment?), as well as for the social dimension (i.e. what are others feeling right now?). In this way, the aim of the Ph.D. project is the development of agent-based models, especially in order to recreate the social mechanisms studied by social and organizational psychology (e.g. teamwork; competition and negotiation), able to take into account the individual differences related to the management of the emotional experiences and affecting decision-making processes.

Differences between Entrepreneurs and Managers in Large Organizations: An Implementation of a Theoretical Multi-Agent Model on Overconfidence Results

The well-known research carried out by Busenitz and Barney (1997) exploring differences in the decision-making processes between entrepreneurs and managers in large organizations has been revisited and redesigned as a starting point to create a computational and theoretical Multi Agent Model (MAM) which shows differences in the decision-making processes. In the original study, researchers showed the presence of a different disposition in incurring in biases and in heuristics by entrepreneurs and managers. In particular, two interesting trend curves on the Overconfidence effect have been realized. Authors concluded by stating that the Overconfidence effect is significantly different in entrepreneurs and managers and helps distinguish between these two work categories. Starting from this conclusion and from their results, a computational and theoretical MAM has been designed, where, as suggested by the authors, different decision-maker agents can incur in the Overconfidence effect with different degrees.

A Simulation of Householders’ Recycling Attitudes Based on the Theory of Planned Behavior

What would induce householders to recycle their waste in a practicable way? This paper investigates the determinants of recycling behavior through the development of an Agent-Based Model (ABM) simulation. Specifically, this contribution examines the effectiveness of a simulation based on different recycling behaviors to better understand the phenomenon. The perspective used takes into consideration the recycling attitude and the sensitivity to social norms based on the Theory of Planned Behavior (TPB) to predict recycling outcomes. As a whole, this paper highlights the dominance of dynamics and interacting aspects of waste management for the formulation of effective recycling public policies based on behavioral aspects. In addition, it illustrates how it is possible to use such empirical models as the Structural Equation Models (SEM) as a starting point for developing ABM simulations that are closer to both theory and reality.