Supplementary Files
Keywords
theories on human decision-making
model documentation
formalisation
natural resource use
How to Cite
Abstract
Incorporating representations of human decision-making that are based on social science theories into social-ecological models is considered increasingly important – yet choosing and formalising a theory for a particular modelling context remains challenging. Here, we reflect on our experiences of selecting, formalising and documenting psychological and economic theories of human decision-making for inclusion in different agent-based models (ABMs) of natural resource use. We discuss the challenges related to four critical tasks: How to select a theory? How to formalise a theory and how to translate it into code? How to document the formalisation? In this way, we present a systematic overview of the choices researchers face when including theories of human decision-making in their ABMs, reflect on the choices we made in our own modelling projects and provide guidance for those new to the field. Also, we highlight further challenges regarding the parameterisation and analysis of such ABMs and suggest that a systematic overview of how to tackle these challenges contributes to an effective collaboration in interdisciplinary teams addressing socio-ecological dynamics using models.
References
Aamodt, A., & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations and System Approaches. AI Communications, 7(1), 39–59. https://doi.org/10.3233/AIC-1994-7104
Abdulkareem, S. A., Augustijn, E. W., Mustafa, Y. T., & Filatova, T. (2018). Intelligent judgements over health risks in a spatial agent‑based model. International Journal of Health Geographics, 17(8), 1–19. https://doi.org/10.1186/s12942-018-0128-x
Ahn, H. (2010). Modeling and Analysis of Affective Influences on Human Experience, Prediction, Decision Making, and Behavior. Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/61929
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Ajzen, I. (2012). Martin Fishbein’s legacy: The reasoned action approach. Annals of the American Academy of Political and Social Science, 640(1), 11–27. https://doi.org/10.1177/0002716211423363
Alonso-Betanzos, A., Sánchez-Maroño, N., Fontenla-Romero, O., Polhill, J. G., Craig, T., Bajo, J., & Corchado, J. M. (2017). Agent-Based Modeling of Sustainable Behaviors. Springer. https://doi.org/10.1007/978-3-319-46331-5
An, L. (2012). Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229, 25–36. https://doi.org/10.1016/j.ecolmodel.2011.07.010
Anderson, C. (2008). The End of Theory, Will the Data Deluge Makes the Scientific Method Obsolete? [online] Available at: https://www.wired.com/2008/06/pb-theory/
Armitage, C. J., & Conner, M. (2000). Social cognition models and health behaviour: a structured review. Psychology and Health, 15, 173–189.
Balke, T., & Gilbert, N. (2014). How Do Agents Make Decisions? A Survey Introduction: Purpose & Goals Dimensions of Comparison. Journal of Artificial Societies and Social Simulation, 17(4), 13. https://doi.org/10.18564/jasss.2687
Bandura, A. (1977). Social learning theory. Prentice-Hall.
Bartkowski, B., & Bartke, S. (2018). Leverage points for governing agricultural soils: A review of empirical studies of European farmers’ decision-making. Sustainability, 10, 3179. https://doi.org/10.3390/su10093179
Bell, A. R., Robinson, D. T., Malik, A., & Dewal, S. (2015). Modular ABM development for improved dissemination and training. Environmental Modelling and Software, 73, 189–200. https://doi.org/10.1016/j.envsoft.2015.07.016
Belton, V., & Stewart, T. (2002). Multiple Criteria Decision Analysis. Springer.
Bopp, C., Engler, A., Poortvliet, P. M., & Jara-rojas, R. (2019). The role of farmers ’ intrinsic motivation in the effectiveness of policy incentives to promote sustainable agricultural practices. Journal of Environmental Management, 244, 320–327. https://doi.org/10.1016/j.jenvman.2019.04.107
Boonstra, W. J., & Hentati-Sundberg, J. (2016). Classifying fishers' behaviour. An invitation to fishing styles. Fish and Fisheries, 17(1), 78–100. http://doi.org/10.1111/faf.12092
Brenner, T. (2006). Agent learning representation: advice on modeling economic learning. Handbook of Computational Economics, 2, 895–947. https://doi.org/10.1016/S1574-0021(05)02018-6
Brown, D. G., & Robinson, D. T. (2006). Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and Society, 11(1), 46. http://www.ecologyandsociety.org/vol11/iss1/art46/
Bubeck, P., Botzen, W. J. W., & Aerts, J. C. J. H. (2012). A Review of Risk Perceptions and Other Factors that Influence Flood Mitigation Behavior. Risk Analysis, 32(9), 1481–1495. https://doi.org/10.1111/j.1539-6924.2011.01783.x
Camerer, C. F., Loewenstein, G., & Rabin, M. (Eds.). (2003). Advances in Behavioral Economics. Princeton University Press.
Caron-Lormier, G., Humphry, R. W., Bohan, D. A., Hawes, C., & Thorbek, P. (2008). Asynchronous and synchronous updating in individual-based models. Ecological Modelling, 212(3–4), 522–527. https://doi.org/10.1016/j.ecolmodel.2007.10.049
Cialdini, R. B., & Goldstein, N. J. (2004). Social Influence: Compliance and Conformity. Annual Review of Psychology, 55(1974), 591–621. https://doi.org/10.1146/annurev.psych.55.090902.142015
Cialdini, R. B., Kallgren, C. A., & Reno, R. R. (1991). A Focus Theory of Normative Conduct: A Theoretical Refinement and Reevaluation of the Role of Norms in Human Behavior. Advances in Experimental Social Psychology, 24, 201–234. https://doi.org/10.1016/S0065-2601(08)60330-5
Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015–1026. https://doi.org/10.1037/0022-3514.58.6.1015
Coats, E. J. (2001). Classic and Contemporary Readings in Social Psychology. Pearson.
Cooke, I. R., Queenborough, S. A., Mattison, E. H. A., Bailey, A. P., Sandars, D. L., Graves, A. R., … Sutherland, W. J. (2009). Integrating socio-economics and ecology: A taxonomy of quantitative methods and a review of their use in agro-ecology. Journal of Applied Ecology, 46(2), 269–277. https://doi.org/10.1111/j.1365-2664.2009.01615.x
Crooks, A., Castle, C., & Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6), 417–430. https://doi.org/10.1016/j.compenvurbsys.2008.09.004
Darity, W.A. (ed., 2008). International encyclopedia of the social sciences. Macmillan.
Darnton, A. (2008). Practical guide: An Overview of behaviour change models and their uses. London. https://doi.org/https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/498065/Behaviour_change_reference_report_tcm6-9697.pdf
Davis, P. K., O’Mahony, A., Gulden, T. R., Osoba, O. A., & Sieck, K. (2018). Priority Challenges for Social and Behavioral Research and Its Modeling. Retrieved from https://www.rand.org/pubs/research_reports/RR2208.html
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2007). Developing theory through simulation methods. The Academy of Management Review, 32(2), 480–499. https://doi.org/10.5465/AMR.2007.24351453
Davis, R., Campbell, R., Hildon, Z., Hobbs, L., & Michie, S. (2015). Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review. Health Psychology Review, 9(4), 323–344. https://doi.org/10.1080/17437199.2014.941722
Dessart, F. J., Barreiro-Hurlé, J., & Bavel, R. Van. (2019). Behavioural factors affecting the adoption of sustainable farming practices: a policy-oriented review. European Review Of Agricultural Economics, 46(3), 417–471. https://doi.org/10.1093/erae/jbz019
Dressler, G., Groeneveld, J., Buchmann, C. M., Guo, C., Hase, N., Thober, J., Frank, K. & Müller, B. (2019). Implications of behavioral change for the resilience of pastoral systems—Lessons from an agent-based model. Ecological Complexity, 40(B), 100710. https://doi.org/10.1016/j.ecocom.2018.06.002
Edmonds, B. (2012). Context in social simulation: why it can’t be wished away. Computational and Mathematical Organization Theory, 18(1), 5–21. https://doi.org/doi:10.1007/s10588-011-9100-z
Edmonds, B. (2017). The Post-Truth Drift in Social Simulation. In Social Simulation 2017. Dublin. Retrieved from http://cfpm.org/file_download/179/SSC_Edmonds_SSC2017-309.pdf
Edmonds, B., & Hales, D. (2003). Replication, replication and replication: Some hard lessons from model alignment. Journal of Artificial Societies & Social Simulation, 6(4), 11. Retrieved from http://jasss.soc.surrey.ac.uk/6/4/11.html
Elrod, T., Johnson, R. D., & White, J. (2004). A new integrated model of noncompensatory and compensatory decision strategies. Organizational Behavior and Human Decision Processes, 95(1), 1–19. https://doi.org/10.1016/j.obhdp.2004.06.002
Elsawah, S., Filatova, T., Jakeman, A. J., Kettner, A. J., Zellner, M. L., Ioannis, N., Hamilton, S. H., Axtell, R. L., Brown, D. G., Gilligan, J. M., Janssen, M. A., Robinson, D. T., Rozenberg, J., Ullah, I. I. T., & Lade, S. J. (2020). Eight grand challenges in socio-environmental systems modeling. Socio-Environmental Systems Modelling, 2, 16226. https://doi.org/10.18174/sesmo.2020a16226
Engler, J. O., Abson, D. J., & von Wehrden, H. (2019). Navigating cognition biases in the search of sustainability. Ambio, 48(6), 605–618. https://doi.org/10.1007/s13280-018-1100-5
Frank, R. H. (1987). If Homo Economicus Could Choose His Own Utility Function , Would He Want One with a Conscience? The American Economic Review, 77(4), 593–604. https://www.jstor.org/stable/1814533
Frederiks, E. R., Stenner, K., & Hobman, E. V. (2015). Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour. Renewable and Sustainable Energy Reviews, 41, 1385–1394. https://doi.org/10.1016/j.rser.2014.09.026
Garnett, C., Crane, D., Brown, J., Kaner, E., Beyer, F., Muirhead, C., Hickman, M., Redmore, J., de Vocht, F., Beard, E. & Michie, S. (2018). Reported theory use by digital interventions for hazardous and harmful alcohol consumption, and association with effectiveness: Meta-regression. Journal of Medical Internet Research, 20(2), e69. https://doi.org/10.2196/jmir.8807
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological Review, 103(4), 650–669. https://doi.org/10.1192/bjpo.bp.115.000224
Gigerenzer, G., & Selten, R. (2001). Bounded Rationality The Adaptive Toolbox. The MIT Press.
Gotts, N. M., & Polhill, J. G. (2009). When and how to imitate your neighbours: Lessons from and for FEARLUS. Journal of Artificial Societies and Social Simulation, 12(3). http://jasss.soc.surrey.ac.uk/12/3/2.html
Graybiel, A. M. (2008). Habits, rituals, and the evaluative brain. Annual Review of Neuroscience, 31, 359–387. https://doi.org/10.1146/annurev.neuro.29.051605.112851
Gräbner, C., Bale, C. S. E., Furtado, B. A., Alvarez-Pereira, B., Gentile, J. E., Henderson, H., & Lipari, F. (2017). Getting the Best of Both Worlds? Developing Complementary Equation-Based and Agent-Based Models. Computational Economics, 53(2), 763–782. https://doi.org/10.1007/s10614-017-9763-8
Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T., & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science, 310(5750), 987–991. https://doi.org/10.1126/science.1116681
Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., Edmonds, B., Ge, J., Giske, J., Groeneveld, J., Johnston, A. S. A., Milles, A., Nabe-Nielsen, J., Polhill, J. G., Radchuk, V., Rohwäder, M. -S., Stillman, R. A., Thiele, J. C., & Ayllón, D. (2020). The ODD Protocol for describing Agent-Based and other simulation models: a second update to improve clarity, replication and structural realism. Journal of Artificial Societies and Social Simulation 23(2), 7 http://jasss.soc.surrey.ac.uk/23/2/7.html
Groeneveld, J., Müller, B., Buchmann, C. M., Dressler, G., Guo, C., Hase, N., Hoffmann, F., John, F., Klassert, C., Lauf, T., Liebelt, V., Nolzen, H., Pannicke, N., Schulze, J., Weise, H., & Schwarz, N. (2017). Theoretical foundations of human decision-making in agent-based land use models – A review. Environmental Modelling & Software, 87, 39–48. https://doi.org/10.1016/j.envsoft.2016.10.008
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A. (2011). Statistical inference for stochastic simulation models - theory and application. Ecology Letters, 14(8), 816–827. https://doi.org/10.1111/j.1461-0248.2011.01640.x
Heckbert, S., Baynes, T., & Reeson, A. (2010). Agent-based modeling in ecological economics. Annals of the New York Academy of Sciences, 1185(1), 39–53. https://doi.org/10.1111/j.1749-6632.2009.05286.x
Hewstone, M., Stroebe, W., & Jonas, K. (2015). An Introduction to Social Psychology. Wiley.
Huber, R., Bakker, M., Balmann, A., Berger, T., Bithell, M., Brown, C., Grêt-Regamey, A., Xiong, H., Le, Q. B., Mack, G., Meyfroidt, P., Millington, J., Müller, B., Polhill, J. G., Sun, Z., Seid, R., Troost, C., & Finger, R. (2018). Representation of decision-making in European agricultural agent-based models. Agricultural Systems, 167, 143–160. https://doi.org/10.1016/j.agsy.2018.09.007
Jager, W. (2000). Modelling consumer behaviour. https://www.rug.nl/research/portal/files/9914467/thesis.pdf
Jager, W. (2007). The four P’s in social simulation, a perspective on how marketing could benefit from the use of social simulation. Journal of Business Research, 60(8), 868–875. https://doi.org/10.1016/j.jbusres.2007.02.003
Jager, W. (2017). Enhancing the realism of simulation (EROS): On implementing and developing psychological theory in social simulation. Journal of Artificial Societies & Social Simulation, 20(3). https://doi.org/10.18564/jasss.3522
Jager, W., Abramczuk, K., Komendant-Brodowska, A., Baczko-Dombi, A., Fecher, B., Sokolovska, N., Spits, T., (2020). Looking into the educational mirror: why computation is hardly being taught in the social sciences, and what to do about it. Proceedings of the Social Simulation Conference 2018, Mainz, Germany.
Jager, W., & Ernst, A. (2017). Social Simulation in Environmental Psychology, Introduction of the special issue. Journal of Environmental Psychology, 52, 114–118. https://doi.org/10.1016/j.jenvp.2017.07.002
Jager, W., Janssen, M. A., De Vries, H. J. M., De Greef, J., & Vlek, C. A. J. (2000). Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model. Ecological Economics, 35, 357–379. https://doi.org/10.1016/S0921-8009(00)00220-2
Janssen, M. A. (2016). Impact of diverse behavioral theories on environmental management: explorations with Daisyworld. In T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, & S. E. Chick (Eds.), Proceedings of the 2016 Winter Simulation Conference, IEEE Press. ISBN 978-1-5090-4484-9
Janssen, M.A. (2017) The Practice of Archiving Model Code of Agent-Based Models, Journal of Artificial Societies and Social Simulation 20(1), 2. http://jasss.soc.surrey.ac.uk/20/1/2.html DOI: 10.18564/jasss.3317
Janssen, M. A., & Baggio, J. A. (2017). Using agent-based models to compare behavioral theories on experimental data: Application for irrigation games. Journal of Environmental Psychology, 52, 194–203. https://doi.org/10.1016/j.jenvp.2016.04.018
Kahneman, D. (2003). Maps of Bounded Rationality: Psychology for Behavioral Economics. The American Economic Review, 93(5), 1449–1475. https://doi.org/10.1257/000282803322655392
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–292. https://doi.org/10.2307/1914185
Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2012). Agent-based simulation of innovation diffusion: A review. Central European Journal of Operations Research, 20(2), 183–230. https://doi.org/10.1007/s10100-011-0210-y
Klabunde, A., & Willekens, F. (2016). Decision-Making in Agent-Based Models of Migration: State of the Art and Challenges. European Journal of Population, 32(1), 73–97. https://doi.org/10.1007/s10680-015-9362-0
Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Babram, M. A. (2018). ODD+2D: An ODD based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2), 9. https://doi.org/10.18564/jasss.3646
Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A general, analytic method for generating robust strategies and narrative scenarios. Management Science, 52(4), 514–528. https://doi.org/10.1287/mnsc.1050.0472
Levine, J., Chan, K. M. A., & Satter, T. (2015). From rational actor to efficient complexity manager: Exorcising the ghost of Homo Economicus with a unified synthesis of cognition research. Ecological Economics, 114, 22–32. https://doi.org/10.1016/j.ecolecon.2015.03.010
Lindenberg, S., & Steg, L. (2007). Normative, gain and hedonic goal frames guiding environmental behavior. Journal of Social Issues, 63(1), 117–137. https://doi.org/10.1111/j.1540-4560.2007.00499.x
Maddux, J. E., & Rogers, R. W. (1983). Protection Motivation and Self-Efficacy: A Revised Theory of Fear Appeals and Attitude Change. Journal of Experimental Social Psychology, 19, 469–479. https://doi.org/10.1016/0022-1031(83)90023-9
Masterson, V. A., Stedman, R. C., Enqvist, J., Tengö, M., Giusti, M., Wahl, D., & Svedin, U. (2017). The contribution of sense of place to social-ecological systems research: A review and research agenda. Ecology and Society, 22(1), 49. https://doi.org/10.5751/ES-08872-220149
Meyfroidt, P. (2013). Environmental cognitions, land change, and social–ecological feedbacks: an overview. Journal of Land Use Science, 8(3), 341–367. https://doi.org/10.1080/1747423X.2012.667452
Milner-Gulland, E. J. (2012). Interactions between human behaviour and ecological systems. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1586), 270–278. https://doi.org/10.1098/rstb.2011.0175
Monroe, K. R. (2001). Paradigm Shift: From Rational Choice to Perspective. International Political Science Review, 22(2), 151–172. https://doi.org/10.1177/0192512101222002
Moore, H. E., & Boldero, J. (2017). Designing Interventions that Last: A Classification of Environmental Behaviors in Relation to the Activities, Costs, and Effort Involved for Adoption and Maintenance. Frontiers in Psychology, 8, 1874. https://www.frontiersin.org/article/10.3389/fpsyg.2017.01874
Muldoon, R., Lisciandra, C., Bicchieri, C., Hartmann, S., & Sprenger, J. (2013). On the emergence of descriptive norms. Politics, Philosophy & Economics, 13(1), 3–22. https://doi.org/10.1177/1470594X12447791
Muelder, H., & Filatova, T. (2018). One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models. Journal of Artificial Societies and Social Simulation, 21(4), 5. https://doi.org/10.18564/jasss.3855
Müller, B., Balbi, S., Buchmann, C. M., de Sousa, L., Dressler, G., Groeneveld, J., Klassert, C. J., Le, Q. B., Millington, J. D. A., Nolzen, H., Parker, D. C., Polhill, J. G., Schlüter, M., Schulze, J., Schwarz, N., Sun, Z., Taillandier, P., & Weise, H. (2014). Standardised and transparent model descriptions for agent-based models: Current status and prospects. Environmental Modelling and Software, 55, 156–163. https://doi.org/10.1016/j.envsoft.2014.01.029
Müller, B., Bohn, F., Dreßler, G., Groeneveld, J., Klassert, C., Martin, R., Schlüter, M., Schulze, J., Weise, H., & Schwarz, N. (2013). Describing human decisions in agent-based models – ODD + D, an extension of the ODD protocol. Environmental Modelling & Software, 48, 37–48. http://dx.doi.org/10.1016/j.envsoft.2013.06.003
Muhar, A., Raymond, C. M., van den Born, R. J. G., Bauer, N., Böck, K., Braito, M., Buijs, A., Flint, C., de Groot, W. T., Ives, C. D., Mitrofanenko, T., Plieninger, T., Tucker, C., & van Riper, C. J. (2018). A model integrating social-cultural concepts of nature into frameworks of interaction between social and natural systems. Journal of Environmental Planning and Management, 61(5–6), 756–777. https://doi.org/10.1080/09640568.2017.1327424
Nyborg, K., Anderies, J. M., Dannenberg, A., Lindahl, T., Schill, C., Schlüter, M., Adger, W. N., Arrow, K. J., Barrett, S., Carpenter, S., Chapin, F. S., Crépin, A.-S., Daily, G., Ehrlich, P., Folke, C., Jager, W., Kautsky, N., Levin, S. A., Madsen, O. J., Polasky, S., Scheffer, M., Walker, B., Weber, E. U., Wilen, J., Xepapadeas, A., & de Zeeuw, A. (2016). Social norms as solutions. Policies may influence large-scale behavioral tipping. Science, 354(6308), 42–43. https://doi.org/10.1126/science.aaf8317
Nyblade, B., O’Mahony, A., & Sieck, K. (2019). Building on Social Science: Theoretic Foundations for Modelers. In P. K. Davis, A. O’Mahony, & J. Pfautz (Eds.), Social-Behavioral Modeling for Complex Systems (pp. 63–99). Wiley. https://doi.org/10.1002/9781119485001.ch4
O’Hare, M., Plevin, R. J., Martin, J. I., Jones, A. D., Kendall, A., & Hopson, E. (2009). Proper accounting for time increases crop-based biofuels’ greenhouse gas deficit versus petroleum. Environmental Research Letters, 4(2), 024001. https://doi.org/10.1088/1748-9326/4/2/024001
Page, S. E. (1997). On Incentives and Updating in Agent Based Models. Computational Economics, 10, 67-87. https://doi.org/10.1023/A:1008625524072
Pavlov, I. (1927). Conditioned reflexes. Oxford University Press, New York, NY, US.
Peterson, M. (2017). An Introduction to Decision Theory, 2nd ed. Cambridge University Press.
Poile, C., & Safayeni, F. (2016). Using computational modeling for building theory: A double edged sword. Journal of Artificial Societies and Social Simulation, 19(3). https://doi.org/10.18564/jasss.3137
Pope, A. J., & Gimblett, R. (2015). Linking Bayesian and Agent-based Models to Simulate Complex Social-ecological Systems in Semi-arid Regions. Frontiers in Environmental Science, 3, 1–9. https://doi.org/10.3389/fenvs.2015.00055
Rangoni, R., & Jager, W. (2017). Social dynamics of littering and adaptive cleaning strategies explored using agent-based modelling. Journal of Artificial Societies and Social Simulation, 20(2), 1. https://doi.org/10.18564/jasss.3269
Risjord, M. (2019). Middle-range theories as models: New criteria for analysis and evaluation. Nursing Philosophy, 20, e12225. https://doi.org/10.1111/nup.12225
Ritzer, G. (ed.) (2012). Encyclopedia of Social Theory. Sage.
Rollins, N. D., Barton, C. M., Bergin, S., Janssen, M. A., & Lee, A. (2014). A computational model library for publishing model documentation and code. Environmental Modelling and Software, 61, 59–64. https://doi.org/10.1016/j.envsoft.2014.06.022
Scarlett, L., Boyd, J., Brittain, A., Shabman, L., & Brennan, T. (2013). Catalysts for Conservation: Exploring Behavioral Science Insights for Natural Resource Investments. Resources for the future report. Retrieved from https://www.rff.org/documents/443/RFF-Rpt-BehavioralScienceEconomicInsights.pdf
Schlüter, M., Baeza, A., Dressler, G., Frank, K., Groeneveld, J., Jager, W., Janssen, M. A., McAllister, R. R. J., Müller, B., Orach, K., Schwarz, N., & Wijermans, N. (2017). A framework for mapping and comparing behavioral theories in models of social-ecological systems. Ecological Economics, 131, 21–35. https://doi.org/10.1016/j.ecolecon.2016.08.008
Schmolke, A., Thorbek, P., DeAngelis, D. L., & Grimm, V. (2010). Ecological models supporting environmental decision making: a strategy for the future. Trends in Ecology & Evolution, 25(8), 479–486. https://doi.org/10.1016/j.tree.2010.05.001
Schulze, J., Müller, B., Groeneveld, J., & Grimm, V. (2017). Agent-Based Modelling of Social-Ecological Systems: Achievements, Challenges, and a Way Forward. Journal of Artificial Societies and Social Simulation, 20(2), 8. https://doi.org/10.18564/jasss.3423
Silverman, E., Bijak, J., & Noble, J. (2011). Feeding the beast: Can computational demographic models free us from the tyranny of data? European Conference on Artificial Life, 276, 747–754. Retrieved from http://eprints.ecs.soton.ac.uk/22839/
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
Simon, H. A. (1978). Rationality as Process and as Product of Thought. The American Economic Review, 68(2), 1–16. https://doi.org/10.1017/CBO9780511598951.005
Skinner, B.F. (1938). The behavior of organisms. Appleton-Century.
Smajgl, A., & Barreteau, O. (2017). Framing options for characterising and parameterising human agents in empirical ABM. Environmental Modelling and Software, 93, 29–41. https://doi.org/10.1016/j.envsoft.2017.02.011
Smith, E. R., & Conrey, F. R. (2007). Agent-based modeling: A new approach for theory building in social psychology. Personality and Social Psychology Review, 11(1), 87–104. https://doi.org/10.1177/1088868306294789
Speelman, E.N., (2014. Gaming and simulation to explore resilience of contested agricultural landscapes. Doctoral Thesis, Wageningen University, Farming Systems Ecology
Steg, L., van den Berg, A. E., & de Groot, J. I. M. (2012). Environmental Psychology: An Introduction. Wiley-Blackwell.
Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3), 309–317. https://doi.org/10.1016/j.jenvp.2008.10.004
Sullivan, A. (2002). Bourdieu and education: How useful is Bourdieu’s theory for researchers? The Netherlands’ Journal of Social Sciences, 38(2), 144–166.
Thiele, J. C., Kurth, W., & Grimm, V. (2014). Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and R. Journal of Artificial Societies and Social Simulation, 17(3), 11. https://doi.org/10.18564/jasss.2503
Turner, B.S., Kyung-Sup, C., Epstein, C.F., Kivisto, P., Outhwaite, W., Ryan, J.M. (eds., 2017). The Wiley Blackwell Encyclopedia of Social Theory. Wiley-Blackwell.
van Vugt, M., Griskevicius, V., & Schultz, P. W. (2014). Naturally Green: Harnessing Stone Age Psychological Biases to Foster Environmental Behavior. Social Issues and Policy Review, 8(1), 1–32. https://doi.org/10.1111/sipr.12000
Watson, A. J., & Lovelock, J. E. (1983). Biological homeostasis of the global environment: the parable of Daisyworld. Tellus B, 35 B(4), 284–289. https://doi.org/10.1111/j.1600-0889.1983.tb00031.x
West, R., Godinho, C. A., Bohlen, L. C., Carey, R. N., Hastings, J., Lefevre, C. E., & Michie, S. (2019). Development of a formal system for representing behaviour-change theories. Nature Human Behaviour, 3, 526-536. https://doi.org/10.1038/s41562-019-0561-2
Wijermans, N., Boonstra, W. J., Orach, K., Hentati-Sundberg, J., & Schlüter, M. (2020). Behavioural diversity in fishing—Towards a next generation of fishery models. Fish and Fisheries, 274(2), 30–19. http://doi.org/10.1111/faf.12466
Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University. Evanston, IL.
World Bank (2015). World Development Report 2015: Mind, Society, and Behavior. Retrieved from http://www.worldbank.org/en/publication/wdr2015
Yu, Q., Verburg, P. H., & Wu, W. (2019). Environmental cognitions mediate the causal explanation of land change. Journal of Land Use Science, 13(5), 535–548. https://doi.org/10.1080/1747423X.2019.1567837
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2020 Nina Schwarz, Gunnar Dressler, Karin Frank, Wander Jager, Marco Janssen, Birgit Müller, Maja Schlüter, Nanda Wijermans, Jürgen Groeneveld