Abstract
Simulations of social processes are a special category of interactions between humans and computers, characterized by the modeling through the latter of the behavior of the former. This paper proposes a framework for studying the development of simulation capabilities by society, as part of an ongoing process of adaptation by complex social systems against a more challenging environment. We begin by discussing the characteristics that the capability to simulate social systems confers in terms of increased effectiveness of the decision-making process of a society. Then, by framing this increased effectiveness as a problem of increased computational capacity by an intelligent agent, we describe the impact that this has on the fitness of an agent that is adapting to a changing environment. We thus provide a mathematical formalization for studying the development of computational sociology in terms of adaptive fitness. This formalization lets us draw the interesting conclusion that, if the adapting system records a decrease in its fitness over its recent past, the formulation of adapting decisions becomes an increasingly more self-referential problem. That is to say, it depends increasingly more on the computation of the outcome of the past actions of the agent, and increasingly less on the behavior of the environment around it. This promotes the theoretical generalization that the development of simulation capabilities for adaptation increases the role that the agent has in determining the status of the agent’s world, and decreases the role that the environment has in the same process.
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References
Patt, A., Siebenhüner, B.: Agent based modeling and adaption to climate change. Vierteljahrshefte zur Wirtschaftsforschung 74, 310–320 (2005)
Oswald, S.M., et al.: Using urban climate modelling and improved land use classifications to support climate change adaptation in urban environments: a case study for the city of Klagenfurt, Austria. Urban Climate 31, 100582 (2020)
Martens, C., et al.: Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies. Glob. Change Biol. 27, 3440–4358 (2020)
Hassani-Mahmooei, B., Parris, B.W.: Climate change and internal migration patterns in Bangladesh: an agent-based model. Environ. Dev. Econ. 17, 763–780 (2012)
Dignum, F., Dignum, V., Jonker, C.M.: Towards agents for policy making. In: David, N., Sichman, J.S. (eds.) Multi-agent-Based Simulation IX, pp. 141–153. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01991-3_11
Thorngate, W., Tavakoli, M.: Simulation, rhetoric, and policy making. Simul. Gaming 40, 513–527 (2009)
Troost, C., Berger, T.: Dealing with uncertainty in agent-based simulation: farm-level modeling of adaptation to climate change in Southwest Germany. Am. J. Agr. Econ. 97, 833–854 (2015)
Conte, R., et al.: Manifesto of computational social science. Eur. Phys. J. Spec. Topics 214, 325–346 (2012)
Wittek, P., Rubio-Campillo, X.: Scalable agent-based modelling with cloud HPC resources for social simulations. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 355–362. IEEE (2012)
Botero, C.A., Weissing, F.J., Wright, J., Rubenstein, D.R.: Evolutionary tipping points in the capacity to adapt to environmental change. Proc. Natl. Acad. Sci. 112, 184–189 (2015)
Donaldson-Matasci, M.C., Lachmann, M., Bergstrom, C.T.: Phenotypic diversity as an adaptation to environmental uncertainty. Evol. Ecol. Res. 10, 493–515 (2008)
Gillings, M.R., Hilbert, M., Kemp, D.J.: Information in the biosphere: biological and digital worlds. Trends Ecol. Evol. 31, 180–189 (2016)
Burkart, J.M., Schubiger, M.N., van Schaik, C.P.: The evolution of general intelligence. Behav. Brain Sci. 40 (2017). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/abs/evolution-of-general-intelligence/5AB1923F6D39AEED6AFB91E5AACCEE8E
Miller, G.: Sexual selection for indicators of intelligence. In: Novartis Foundation Symposium, pp. 260–270. Wiley Online Library (2000)
Chollet, F.: On the measure of intelligence. arXiv preprint arXiv:1911.01547 (2019)
Keuschnigg, M., Lovsjö, N., Hedström, P.: Analytical sociology and computational social science. J. Comput. Soc. Sci. 1, 3–14 (2018)
Chen, S.-H.: Agent-Based Computational Economics: How the Idea Originated and Where it is Going. Routledge, London (2017)
Gilbert, N., Ahrweiler, P., Barbrook-Johnson, P., Narasimhan, K., Wilkinson, H.: Computational modelling of public policy: reflections on practice. J. Artif. Soc. Soc. Simul. 21, 1–14 (2018)
Preise, R., Biggs, R., De Vos, A., Folke, C.: Social-ecological systems as complex adaptive systems: organizing principles for advancing research methods and approaches (2018)
Simon, H.A.: The architecture of complexity. In: Klir, G.J. (ed.) Facets of Systems Science, pp. 457–476. Springer, Boston (1991). https://doi.org/10.1007/978-1-4899-0718-9_31
Holland, J.H.: Complex adaptive systems. Daedalus 121, 17–30 (1992)
Miller, J.H., Page, S.E.: Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press, Princeton (2009)
Weisberg, M.: Simulation and Similarity: Using Models to Understand the World. Oxford University Press, Oxford (2012)
Smit, B., Burton, I., Richard, J.T., Klein, J.W.: An anatomy of adaptation to climate change and variability. In: Kane, S.M., Yohe, G.W. (eds.) Societal Adaptation to Climate Variability and Change, pp. 223–251. Springer, Dordrecht (2000). https://doi.org/10.1007/978-94-017-3010-5_12
Badjeck, M.-C., Allison, E.H., Halls, A.S., Dulvy, N.K.: Impacts of climate variability and change on fishery-based livelihoods. Mar. Policy 34, 375–383 (2010)
Kratz, T.K., Deegan, L.A., Harmon, M.E., Lauenroth, W.K.: Ecological variability in space and time: insights gained from the US LTER program. Bioscience 53, 57–67 (2003)
Hendry, A.P., Schoen, D.J., Wolak, M.E., Reid, J.M.: The contemporary evolution of fitness. Annu. Rev. Ecol. Evol. Syst. 49, 457–476 (2018)
Folland, C.K., Karl, T.R., Jim Salinger, M.: Observed climate variability and change. Weather 57, 269–278 (2002)
Carley, K.M.: Simulating society: the tension between transparency and veridicality. In: Proceedings of Agents, p. 2 (2002)
Mark, J.T., Marion, B.B., Hoffman, D.D.: Natural selection and veridical perceptions. J. Theor. Biol. 266, 504–515 (2010)
Nour, M.M., Nour, J.M.: Perception, illusions and bayesian inference. Psychopathology 48, 217–221 (2015)
Larzelere, A.R.: Creating simulation capabilities. IEEE Comput. Sci. Eng. 5, 27–35 (1998)
Harzheim, E.: Ordered Sets. Springer, Heidelberg (2006). https://doi.org/10.1007/b104891
Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Machine Learning Proceedings 1994, pp. 157–163. Elsevier (1994)
Macy, M.W., Willer, R.: From factors to actors: computational sociology and agent-based modeling. Ann. Rev. Sociol. 28, 143–166 (2002)
Piantadosi, C.A.: The Biology of Human Survival: Life and Death in Extreme Environments. Oxford University Press, Oxford (2003)
Rockwood, L.L.: Introduction to Population Ecology. John Wiley & Sons, Hoboken (2015)
Grafius, D.R., et al.: Estimating food production in an urban landscape. Sci. Rep. 10, 1–9 (2020)
Maud, P.J., Foster, C.: Physiological assessment of human fitness. Human Kinetics (2006)
Maclean, K., Cuthill, M., Ross, H.: Six attributes of social resilience. J. Environ. Planning Manage. 57, 144–156 (2014)
Zhang, Z., Wang, D., Zhao, D., Song, T.: FMR-GA – a cooperative multi-agent reinforcement learning algorithm based on gradient ascent. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S. (eds.) Neural Information Processing, pp. 840–848. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70087-8_86
McLeman, R., Smit, B.: Migration as an adaptation to climate change. Clim. Change 76, 31–53 (2006)
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De Luca, G., Lampoltshammer, T.J., Parven, S. (2021). Why Developing Simulation Capabilities Promotes Sustainable Adaptation to Climate Change. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2021. Lecture Notes in Computer Science(), vol 12797. Springer, Cham. https://doi.org/10.1007/978-3-030-77772-2_32
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