Agent Based Modelling for Simulating the Interregional Patient Mobility in Italy.

Patient mobility is considered one of the main concerns for policy-makers as it impacts financial sustainability of regional health systems due to the high percentage of patients accessing care services in other regions. For a better understanding of this phenomenon, it is necessary to define a behavioral model able to represent the patient-system interaction. In this paper we adopted the Agent-Based Modelling (ABM) approach with the aim of simulating patient flow across regions and determining which are the main factors influencing it. This may provide a new insight for policy makers to capture which are the main factors influencing mobility and actions that may contribute to contain this phenomenon.

Evidence from a long-term experiment that collective risks change social norms and promote cooperation.

Social norms can help solve pressing societal challenges, from mitigating climate change to reducing the spread of infectious diseases. Despite their relevance, how norms shape cooperation among strangers remains insufficiently understood. Influential theories also suggest that the level of threat faced by different societies plays a key role in the strength of the norms that cultures evolve. Still little causal evidence has been collected. Here we deal with this dual challenge using a 30-day collective-risk social dilemma experiment to measure norm change in a controlled setting. We ask whether a looming risk of collective loss increases the strength of cooperative social norms that may avert it. We find that social norms predict cooperation, causally affect behavior, and that higher risk leads to stronger social norms that are more resistant to erosion when the risk changes. Taken together, our results demonstrate the causal effect of social norms in promoting cooperation and their role in making behavior resilient in the face of exogenous change.

Reputation or peer review? The role of outliers.

We present an agent-based model of paper publication and consumption that allows to study the effect of two different evaluation mechanisms, peer review and reputation, on the quality of the manuscripts accessed by a scientific community. The model was empirically calibrated on two data sets, mono- and multi-disciplinary. Our results point out that disciplinary settings differ in the rapidity with which they deal with extreme events-papers that have an extremely high quality, that we call outliers. In the mono-disciplinary case, reputation is better than traditional peer review to optimize the quality of papers read by researchers. In the multi-disciplinary case, if the quality landscape is relatively flat, a reputation system also performs better. In the presence of outliers, peer review is more effective. Our simulation suggests that a reputation system could perform better than peer review as a scientific information filter for quality except when research is multi-disciplinary and in a field where outliers exist.

Mechanism change in a simulation of peer review: from junk support to elitism.

Peer review works as the hinge of the scientific process, mediating between research and the awareness/acceptance of its results. While it might seem obvious that science would regulate itself scientifically, the consensus on peer review is eroding; a deeper understanding of its workings and potential alternatives is sorely needed. Employing a theoretical approach supported by agent-based simulation, we examined computational models of peer review, performing what we propose to call redesign, that is, the replication of simulations using different mechanisms. Here, we show that we are able to obtain the high sensitivity to rational cheating that is present in literature. In addition, we also show how this result appears to be fragile against small variations in mechanisms. Therefore, we argue that exploration of the parameter space is not enough if we want to support theoretical statements with simulation, and that exploration at the level of mechanisms is needed. These findings also support prudence in the application of simulation results based on single mechanisms, and endorse the use of complex agent platforms that encourage experimentation of diverse mechanisms.

On agent-based modeling and computational social science.

In the first part of the paper, the field of agent-based modeling (ABM) is discussed focusing on the role of generative theories, aiming at explaining phenomena by growing them. After a brief analysis of the major strengths of the field some crucial weaknesses are analyzed. In particular, the generative power of ABM is found to have been underexploited, as the pressure for simple recipes has prevailed and shadowed the application of rich cognitive models. In the second part of the paper, the renewal of interest for Computational Social Science (CSS) is focused upon, and several of its variants, such as deductive, generative, and complex CSS, are identified and described. In the concluding remarks, an interdisciplinary variant, which takes after ABM, reconciling it with the quantitative one, is proposed as a fundamental requirement for a new program of the CSS.