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References

Anderson, P. (1972). More is different. Science, 177(4047), 393–396.
Bardone, E. (2016). Intervening via chance-seeking. In D. Secchi & M. Neumann (Eds.), Agent-
based simulation of organizational behavior. New frontiers of social science research (pp. 203–
220). New York: Springer.
Bland, J. M. (2009). The tyranny of power: Is there a better way to calculate sample size? BMJ,
339, b3985.
Champely, S., Ekstrom, C., Dalgaard, P., Gill, J., Weibelzahl, S., & Rosario, H. D. (2016). Pwr:
Basic functions for power analysis.
Choirat, C., & Seri, R. (2012). Estimation in discrete parameter models. Statistical Science, 27(2),
278–293.
Coen, C. (2009). Simple but not simpler. Introduction CMOT special issue–simple or realistic.
Computational and Mathematical Organization Theory, 15, 1–4.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: LEA.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159.
Cohen, M. D., March, J. G., & Olsen, H. P. (1972). A garbage can model of organizational choice.
Administrative Science Quarterly, 17(1), 1–25.
Davidsson, P., & Verhagen, H. (2017). Types of simulation. doi: https://doi.org/10.1007/978-3-
319-66948-9_3.
de Marchi, S., & Page, S. E. (2014). Agent-based models. Annual Review of Political Science,
17(1), 1–20.
Edmonds, B., & Meyer, R. (2017). Introduction to the handbook. doi: https://doi.org/10.1007/978-
3-319-66948-9_1.
Edmonds, B., & Moss, S. (2005). From KISS to KIDS — an ‘anti-simplistic’ modelling approach.
In P. Davidson (Ed.), Multi agent based simulation. Lecture Notes in Artificial Intelligence
(Vol. 3415, pp. 130–144). New York: Springer.
Erdfelder, E. (1984). Zur Bedeutung und Kontrolle des ˇ-Fehlers bei der inferenzstatistischen
Prüfung log-linearer Modelle [The significance and control of the ˇ-error during the inference-
statistical examination of the log-linear models]. Zeitschrift für Sozialpsychologie, 15(1),
18–32.
Fioretti, G. (2016). Emergent organizations. In D. Secchi & M. Neumann (Eds.), Agent-based
simulation of organizational behavior. New frontiers of social science research (pp. 19–41).
New York: Springer.
Fioretti, G., & Lomi, A. (2008). An agent-based representation of the garbage can model of
organizational choice. Journal of Artificial Societies and Social Simulation, 11(1), 1.
Fioretti, G., & Lomi, A. (2010). Passing the buck in the garbage can model of organizational
choice. Computational and Mathematical Organization Theory, 16(2), 113–143
Fisher, R. (1955). Statistical methods and scientific induction. Journal of the Royal Statistical
Society. Series B (Methodological), 17(1), 69–78
Gigerenzer, G. (2004). Mindless statistics. Journal of Socio-Economics, 33, 587–606.
Gilbert, N., & Terna, P. (2000). How to build and use agent-based models in social science. Mind
and Society, 1, 57–72.
Hahn, G. J., & Meeker, W. Q. (2011). Statistical intervals: A guide for practitioners. Hoboken:
Wiley.
Heckbert, S. (2013). MayaSim: An agent-based model of the ancient Maya social-ecological
system. Journal of Artificial Societies and Social Simulation, 16(4), 11.
Herath, D., Secchi, D., & Homberg, F. (2015). Simulating the effects of disorganisation on
employee goal setting and task performance. In D. Secchi & M. Neumann (Eds.), Agent-based
simulation of organizational behavior. New frontiers of social science research (pp. 63–84).
New York: Springer.