Simulating the Social Processes of Science

Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.Simulation, Science, Science and Technology Studies, Philosophy, Sociology, Social Processes

Two Challenges in Simulating the Social Processes of Science

This note discusses two challenges to simulating the social process of science. The first is developing an adequately rich representation of the underlying Data Generation Process which scientific progress can \"learn\". The second is how to get effective data on what, in broad terms, the properties of the \"future\" are. Paradoxically, with due care, we may learn a lot about the future by studying the past.Simulating Science, Algorithmic Chemistry, Evolutionary Algorithms, Data Structures, Learning Systems

A Brief Survey of Some Relevant Philosophy of Science

This briefly reviews some philosophy of science that might be relevant to simulating the social processes of science. It also includes a couple of examples from the sociology of science because these are inextricable from the philosophy.Philosophy, Science, Simulation, Social Processes, Evolutionary Models, Sociology

Using Social Simulation to Explore the Dynamics at Stake in Participatory Research

This position paper contributes to the debate on perspectives for simulating the social processes of science through the specific angle of participatory research. This new way of producing science is still in its infancy and needs some step back and analysis, to understand what is taking place on the boundaries between academic, policy and lay worlds. We argue that social simulation of this practice of cooperation can help in understanding further this new way of doing science, building on existing experience in simulation of knowledge flows as well as pragmatic approaches in social sciences.Participatory Research, Institutional Analysis and Design, Knowledge Flow, Agent Based Simulation

Opinion dynamics: models, extensions and external effects

Recently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. [...]Comment: 42 pages, 6 figure

Computer simulation in archaeology. Art, science or nightmare?

[EN] By simulating historical processes and not just the archaeological material, I intend to explain social causality at the micro and macro levels. The target is no more an archaeological artifact but a human society at large, although existing only in the virtual world. A new target, the artificial society, is created with its own structure and behavior. With the possibility of simulating virtual social systems, a new methodology of scientific inquiry becomes possible.[ES] Se propone ampliar el concepto mismo de Arqueología Virtual a la simulación de sociedades humanas y no sólo la simulación de objetos que existieron en el pasado. La idea es poder disponer de una herramienta para explicar las formas de causalidad social a un nivel tanto micro como macro. El objetivo, por tanto, ya no es el artefacto arqueológico, sino la sociedad en su sentido más amplio, si bien se trata de una sociedad que sólo existe en un mundo “virtual”. Esta sociedad artificial, nuevo objetivo del análisis se crea con su propia estructura y conducta simuladas. Con la posibilidad de simular sistemas sociales virtuales, se hace posible una nueva metodología para la investigación científica.This research is funded by the Spanish Ministry for Scienc and Innovation, under grant No. HAR2009-12258, and it is a part of the joint research team “Social and environmental transitions: Simulating the past to understand human behaviour (SimulPast)”(www.simulpast.es), funded by the same national agency under the program CONSOLIDER-INGENIO 2010, CSD2010-00034.Barceló, JA. (2012). Computer simulation in archaeology. Art, science or nightmare?. Virtual Archaeology Review. 3(5):8-12. https://doi.org/10.4995/var.2012.4489OJS81235ALTAWEEL, M. (2008): Investigating agricultural sustainability and strategies in northern Mesopotamia: results produced using a socio-ecological modeling approach, Journal of Archaeological Science nº 35, pp. 821-835. http://dx.doi.org/10.1016/j.jas.2007.06.012BARCELÓ, J.A. (2009): Computational Intelligence in Archaeology. Hershey (NY): The IGI Group. http://dx.doi.org/10.4018/978-1-59904-489-7BARCELÓ, J.A.; CUESTA, J.A:; DEL CASTILLO, F. GALÁN, J.M.; MAMELI, L., MIGUEL, F.; SANTOS, J. I.; VILÀ, X.. (2010): "Patagonian Ethnogenesis: towards a computational simulation approach", in Proceedings of the 3rd World Congress on Social Simulation WCSS2010 CDROM.. Kassel: Center for Environmental Systems Research, University of Kassel; pp. 1-9.CHRISTIANSEN J.H., ALTAWEEL M, (2006): "Simulation of natural and social process interactions: An example from Bronze Age Mesopotamia". Social Science Computer Review. 24(2), pp. 209-226. http://dx.doi.org/10.1177/0894439305281500COSTOPOULOS, A. LAKE, M.V. (2010): Simulating Change Archaeology Into the Twenty-first Century Salt Lake, The University of Utah Press.DORAN JM, PALMER M, GILBERT N, & MELLARS P, (1994): "The EOS project: Modeling Upper Paleolithic social change". In Gilbert N and Doran J, eds. Simulating Societies: The Computer Simulation of Social Phenomena, London: UCL Press, pp. 195-221.EDMONDS, B., MOSS, S. (2011): Simulating Social Complexity: A Handbook. Berlin, Springer.EPSTEIN, J. M. (2006): Generative Social Science: Studies in Agent-Based Computational Modeling). Princeton University Press.GUMERMAN, G. J., SWEDLUND, A. C., DEAN, J. S, EPSTEIN J. M. (2003): The Evolution of Social Behavior in the Prehistoric American Southwest. Artificial Life 9, pp. 435-444. http://dx.doi.org/10.1162/106454603322694861JANSSEN, M.A, (2009): "Understanding Artificial Anasazi", in Journal of Artificial Societies and Social Simulation 12 (4) 13 http://jasss.soc.surrey.ac.uk/12/4/13.htmlKOHLER, T.A., KRESL, J., WEST, C.V., CARR, E., WILSHUSEN, R. (2000): Be there then: A modeling approach to settlement determinants and spatial efficiency among late ancestral Pueblo populations of the Mesa Verde region, U. S. Southwest. In Kohler TA and Gumerman GJ, eds. Dynamics inHuman and Primate Societies: Agent-Based Modeling of Social and Spatial Processes, New York: Oxford University Press, pp. 145-178.KOHLER, T. A., LEEUW, S. v.d. (2007): The Model-Based Archaeology of Socionatural Systems. Santa Fe: School of Advanced Research Press.KOHLER, T A, VARIEN, M. D., WRIGHT, A., KUCKELMAN, K. A. (2008): Mesa Verde Migrations: New archaeological research and computer simulation suggest why ancestral Puebloans deserted the northern Southwest United States. American Scientist nº 96, pp. 146-153. http://dx.doi.org/10.1511/2008.70.3641MACY, M. W. WILLER, R. (2002): "From Factors to Actors: Computational Sociology and Agent-Based Models", en Annual Review of Sociology, nº 28, pp. 143-166.SAWYER, R. K. (2005): Social emergence: societies as complex systems. New York: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511734892SOKOLOWSKI, J.A., BANKS, C.M., (2009): Modeling and Simulation for Analyzing Global Events London, Wiley. http://dx.doi.org/10.1002/9780470486993ZACHARIAS, G.L., MACMILLAN, M., VAN HEMEL, S.B. (2008): Behavioral Modeling and Simulation: From Individuals to Societies, Washington, National Academies Press

Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

Dynamical trust and reputation computation model for B2C E-Commerce

Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce