Cognition & ICT  (CI) Session 2

Abstract: Policing interfaces with a variety of multilevel complex systems including: heterogeneous local, national and international criminality; local and national government policy on crime; budgets and targets for crime containment; and public perceptions of crime and social safety. When a UK police force identifies a group of individuals suspected of involvement in organised crime, it undertakes a nationally standardised ‘mapping’ procedure. This involves entering details of the group members' known and suspected activity, associates and capability into a spreadsheet. A numerical score is then calculated so that each organised crime group (OCG) can be placed into one of several ‘bands’ which reflect the range and severity of crime in which the group is involved as well as its level of capability and sophistication. This paper is based on an academic-police research collaboration that is investigating the ‘Organised Crime Group Mapping’ (OCGM) data for one of the UK’s largest police forces. The existing data analysis procedures are being evaluated using a novel combination of systems thinking and complexity methods. These methods include the problem-oriented perspective of applied systems theory that sets system boundaries in the context of policy problems, combined with the perspective of multilevel multidimensional dynamics of network and hypernetwork theory. The research therefore sits firmly in the context of policy, data analysis and practical policing. This presentation will sketch the many subsystems involved in the UK's Serious and Organised Crime Strategy and give overview of the analytic approach. The focus will be on new results being obtained from the OCGM data, how the systems-complexity methods can be extended and used within practical policing, and the implications for policy. Close

Abstract: Massive digital misinformation is one of the main threats to our society, according to the World Economic Forum. Our recent studies [1-2] show that users online tend to select information by confirmation bias and to join virtual echo chambers where they reinforce and polarize their beliefs. On one hand, social media have the power to inform, engage or mobilize, but on the other hand also to misinform, manipulate or control. In such media without mediation, the public has to deal with a large amount of misleading information generated by nationalists, populists and conspirators, that is corrupting reliable sources. Last but not least, discussions between like-minded people only reinforce their positions, thus bursting polarization. Indeed, our recent work [3] shows that a negative emotional pattern is generally observed when polarized communities interact on the Italian Facebook. In this work we present our most recent advancements about the quantitative understanding of collective framing online by addressing the emotional dynamics of 54 Million users around two distinct kinds of narratives — scientific and conspiracy news — on US Facebook. We introduce a new metric to analyze the emotional polarization of both users and posts, which successfully reveals heavily opinionated users and posts on controversial topics. Furthermore, we measure the emotional impact of information in contrast with one’s beliefs, showing that users tend to react negatively to the correction attempts. Although online discussions are open to anyone, users only rarely discuss their opinions outside their echo chambers. [1] Bessi et al. "Science vs conspiracy: Collective narratives in the age of misinformation." PLoS ONE 10.2 (2015):e0118093. [2] Del Vicario et al. "The spreading of misinformation online." PNAS 113.3 (2016):554-559. [3] Zollo et al. (2015) Emotional Dynamics in the Age of Misinformation. PLoS ONE 10(9):e0138740. Close

Abstract: With the advent of online networks, societies are substantially more connected with individual members able to easily modify and maintain their own social links. Here, we show that active network maintenance exposes agents to confirmation bias, the tendency to confirm one’s beliefs, and we explore how this affects collective opinion formation. We introduce a model of binary opinion dynamics on a complex network with fast, stochastic rewiring and show that confirmation bias induces a segregation of individuals with different opinions. We use the dynamics of global opinion to generally categorize opinion update rules and find that confirmation bias always stabilizes the consensus state. Finally, we show that the time to reach consensus has a non-monotonic dependence on the magnitude of the bias, suggesting a novel avenue for large-scale opinion engineering. Close

Abstract: Nowadays, Evolutionary Game Theory (EGT) represents a field of growing interest in different scientific communities, as biology and social science. On the other hand, the Darwinian concept of evolution, underlying the dynamics of evolutionary games, represents a powerful inspiring source also in the field of natural computing (e.g. genetic algorithms, swarm logic and ant colonies) for solving optimization problems. The latter have been widely investigated also within the realm of statistical physics, where theoretical physics and information theory meet forming a powerful framework for studying complex systems. In this work ([1]), we present a new heuristic based on the Public Goods Game (PGG) for solving problems as the Traveling Salesman Problem (TSP). In particular, the order-disorder phase transition occurring in population interacting by the classical PGG can be adopted for letting the population to converge towards a common solution of a given TSP. Notably, the solution plays the same role of the strategy in the PGG, and the order is reached by implementing a mechanism of partial imitation (i.e. agents imitate richer agents). Remarkably, results of numerical simulations show that it is possible to compute both optimal and sub-optimal solutions, on varying the number of cities in the TSP and the amount of agents in the population. Therefore, in the light of the achieved outcomes, we deem relevant to further investigate the potential of evolutionary games in optimization problems, enlarging the domain of application of EGT. To conclude, beyond to present our results, we aim to show basic principles of EGT and their potential applications in other fields, so that the presentation be of interest for scientists coming from different communities. [1] Javarone MA: Solving Optimization Problems by the Spatial Public Goods Game. arxiv:1604.02929 (2016) Close