From social simulation to integrative system design

Abstract

The purpose of this White Paper of the EU Support Action "Visioneer" (see www.visioneer.ethz.ch) is to address the following goals: 1. Develop strategies to build up social simulation capacities. 2. Suggest ways to build up an "artificial societies" community that aims at simulating real and alternative societies by means of supercomputers, grid or cloud computing. 3. Derive proposals to establish centers for integrative systems design. 1 Introduction 1.1 Real-world challenges Since decades, if not since hundreds of years, the world has been facing a number of recurrent socioeconomic problems which are obviously hard to solve because their roots are still not well understood. This includes, in particular, the following problems: 1. Financial and economic (in)stability (government debts, taxation, and inflation/deflation; sustainability of social benefit systems; consumption and investment behavior.. .) 2. Public health (food safety; spreading of epidemics [flu, SARS, H1N1, HIV], obesity, smoking, or unhealthy diets.. .) 3. Sustainable use of resources and environment (consumption habits, travel behavior, sustainable and efficient use of energy and other resources, participation in recycling efforts, environmental protection.. .) 4. Collective social behavior and opinion dynamics (abrupt changes in consumer behavior; social contagion, extremism, hooliganism, changing values; breakdown of cooperation, trust, compliance, solidarity.. .) 5. Security and peace (organized crime, terrorism, social unrest, independence movements, conflict, war.. .) 6. Reliability of critical infrastructures, in particular of information systems (cyber risks, misuse of sensitive data, espionage, violation of privacy; data deluge, spam; education and inheritance of culture.. .

When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents

“…While this interdisciplinary interaction between engineering, particularly computer science and robotics, and biology is now commonplace (Di Caro and Dorigo 1998;Foukia et al 2003;Di Caro et al 2004, January;Gerkey and Matarić 2004;Mullen et al 2009;Rubenstein et al 2014), it is to our knowledge still rare for the science of organizations or management and biology, and in many of the existing cases, production processes, not the human workers themselves, are modeled as the equivalent to the 'workers' discussed here (Fulkerson and Staffend 1997;Cicirello and Smith 2004;Frayret et al 2004). Perhaps this is in part due to the difficulty of translating research findings from biology to the social sciences and vice versa (though see: Pugh et al 1969;Levchuk et al 2002;Helbing and Balietti 2011), or perhaps due to the fact that in designing optimal strategies for human organizations, the selfish motives of individuals play an overshadowing role (Itoh 1992; note that "selfish" is used here not in a moral sense, but purely in the sense that individual payoffs, as well as group-level performance, have to be optimized in human organizations, unlike in social insect organizational strategies). However, such questions as what is the ideal ratio of specialists to generalists, how does fluctuation in demand for different jobs affect the optimal level of specialization, and may there be benefits in terms of efficiency, flexibility, or robustness to having 'surplus' workers, are universal across many social and engineered complex systems.…”

On agent-based modeling and computational social science

“…Rather than a detailed survey of ABM (for a good example, see Helbing and Balietti, 2011a) this paper presents an attempt to draw a balance of this field, pointing to its main weaknesses and strengths.…”

FuturICT: Participatory computing to understand and manage our complex world in a more sustainable and resilient way

“…FuturICT is often confronted with questions regarding the predictability of its models [5][6][7]. Recent findings suggest that the dynamics in social systems depends at least on four different factors: the situational context, interaction effects, history, and random factors.…”