Conformity-Driven Agents Support Ordered Phases in the Spatial Public Goods Game

We investigate the spatial Public Goods Game in the presence of fitness-driven and conformity-driven agents. This framework usually considers only the former type of agents, i.e., agents that tend to imitate the strategy of their fittest neighbors. However, whenever we study social systems, the evolution of a population might be affected also by social behaviors as conformism, stubbornness, altruism, and selfishness. Although the term evolution can assume different meanings depending on the considered domain, here it corresponds to the set of processes that lead a system towards an equilibrium or a steady-state. We map fitness to the agents' payoff so that richer agents are those most imitated by fitness-driven agents, while conformity-driven agents tend to imitate the strategy assumed by the majority of their neighbors. Numerical simulations aim to identify the nature of the transition, on varying the amount of the relative density of conformity-driven agents in the population, and to study the nature of related equilibria. Remarkably, we find that conformism generally fosters ordered cooperative phases and may also lead to bistable behaviors.Comment: 13 pages, 5 figure

Erratic Flu Vaccination Emerges from Short-Sighted Behavior in Contact Networks

The effectiveness of seasonal influenza vaccination programs depends on individual-level compliance. Perceptions about risks associated with infection and vaccination can strongly influence vaccination decisions and thus the ultimate course of an epidemic. Here we investigate the interplay between contact patterns, influenza-related behavior, and disease dynamics by incorporating game theory into network models. When individuals make decisions based on past epidemics, we find that individuals with many contacts vaccinate, whereas individuals with few contacts do not. However, the threshold number of contacts above which to vaccinate is highly dependent on the overall network structure of the population and has the potential to oscillate more wildly than has been observed empirically. When we increase the number of prior seasons that individuals recall when making vaccination decisions, behavior and thus disease dynamics become less variable. For some networks, we also find that higher flu transmission rates may, counterintuitively, lead to lower (vaccine-mediated) disease prevalence. Our work demonstrates that rich and complex dynamics can result from the interaction between infectious diseases, human contact patterns, and behavior

You Only Die Once: Managing Discrete Interdependent Risks

This paper extends our earlier analysis of interdependent security issues to a general class of problems involving discrete interdependent risks with heterogeneous agents. There is a threat of an event that can only happen once, and the risk depends on actions taken by others. Any agent's incentive to invest in managing the risk depends on the actions of others. Security problems at airlines and in computer networks come into this category, as do problems of risk management in organizations facing the possibility of bankruptcy, and individuals' choices about whether to be vaccinated against an infectious disease. Surprisingly the framework also covers certain aspects of investment in R&D. Here we characterize Nash equilibria with heterogeneous agents and give conditions for tipping and cascading of equilibria.

Modelling of Human Behaviour and Response to the Spread of Infectious Diseases

We incorporate two types of human behavioural changes into the epidemic models. First, a two-subpopulation imitation dynamic model is constructed via the replicator dynamical equations to study the self-initiated pre-cautionary health protective behaviour under the cost-benefit considerations and group pressure. Second, the impacts of additional characteristics of imperfect vaccine and the asymmetric property of smoothed best response on the vaccination behaviour are studied within the vaccination population game framework, and via the Gompertz function, respectively