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 \u201cDecalogue\u201d for those interested in designing agents capable of mimicking human emotional behaviors. In the paper, compassion will be translated as prosocial behavior

Organic food purchase behavior: The complex relationship between consumer\u2019s attitude and social norms.

During the last decade the purchase of green food within a sustainable consumption context has gained momentum. In particular, consumers\u2019 preference toward organic food represents a form of behavior that can both promote the preservation of the environment and contribute to the transition to a more sustainable society. Certainly, the choice for a specific type of food is based on personal beliefs, but it is also influenced by the social dimension. In relation to this latter aspect, a current issue regarding the understanding and prediction of green consumer behavior is strongly related with the investigation of the effect exercised by group norms and collective consumption (Peattie, 2010). In line with this premise, the Doctoral project aimed to investigate the emergence of sustainable consumption behaviors by considering both the individual and social aspects. Specifically, the project examined the complex relationship that emerges from the dynamic interaction of individual behaviors and social norms in the specific context of organic food choice. Since systematic experimentation over time with social influence is difficult, the research employed virtual simulations: to this purpose, an interdisciplinary approach between psychological methods and computer sciences was adopted. The first phase of the Doctoral project examined those psychological theories able to explain and predict consumers\u2019 intention to buy organic food products. Accordingly, the work by Scalco, Noventa, Sartori and Ceschi (2017) showed by means of a meta-analytical structural equation model the robustness of the theory of planned behavior (TPB; Ajzen, 1991) in this specific context. Therefore, the TPB was assumed as the main theoretical framework of the project. The second phase addressed the potential conjunctions between psychological notions and computer simulations. Particularly, agent-based modeling represents a method of investigation of social phenomena that blends the knowledge of social sciences with the advantages of virtual simulations. Within this context, the development of algorithms able to emulate a realistic reasoning process for autonomous virtual agents is one of the most fragile aspects. The paper by Scalco, Ceschi, and Sartori (2017) specifically dealt with the translation of the theory of planned behavior into a computational form: several issues are discussed and some solutions are offered when available with the hope to shorten the distance between psychological research and the methods provided by computer sciences. Finally, starting from the findings provided by the first work and the theoretical examination conducted in the second paper, an agent-based model was built to investigate how social interactions in relation to organic food products can foster/hinder individual buying behavior among customers of grocery stores with different food arrangements. Virtual consumers in the simulation replicate a decision-making process grounded on the theory of planned behavior: each agent decides to buy conventional/green food on the base of its individual preferences and the social influence exercised by others. The agent-based model showed the effects of social influence on individual behavior: a part of the agents would like to buy green products following their individual preferences, however, the common norm hampers this intention. Consequently, these agents decide to buy regular food instead of green one triggering in this way a locked-in vicious cycle. More interesting, the simulation demonstrated that different arrangements of products can significantly affect the sales of organic food: nonetheless, the increase of sales of organic food also depends on the throng of customers inside the store. In the end, the research improves the understanding regarding the effects of social norms on individual intention to purchase green food. In addition, it attempts to suggest how to foster organic food purchase starting from the results obtained from the simulation. As a further consideration, the Doctoral thesis tried to demonstrate the advantages of the introduction of agent-based modeling as a valuable method for psychological research in relation to the investigation of social phenomena and consumer behavior