Successful Resource Seeking Strategies: An Agent Based Model of Budgetary Competition
The strategies that bureaucratic actors employ to secure resources are the result of a complex interplay between motivational states and environmental conditions. The strategies employed by bureaucrats to secure resources are now best understood as heuristics. Heuristics that may be adaptive in securing resources under some conditions may be maladaptive under different environmental circumstances (Gigerenzer 2000; 2008). This study reviews the various strategies employed by bureaucrats to secure financial resources through the lens of Downs’ typology of bureaucrats to determine the fundamental heuristics the successful strategies employ. We sought inspiration from both the extant literature and models of bureaucratic behavior within organizations beginning with Downs (1967) and continuing with the work of Bowling, Cho, and Wright (2004), and the methodological innovations afforded by agent-based modeling. By making certain basic assumptions regarding decision-making heuristics, we show a remarkable consistency between Downs, Bowling and her colleagues, and our own findings.
2. Bingham, C. & Eisenhardt, K. (2011). Rational heuristics: the ‘simple rules’ that strategists learn from process experience. Strategic Management Journal, 32, 1437-1464.
3. Blom-Hansen, J., Morton, R. & Serritzlew, S. (2015). Experiments in Public Management Research. International Public Management Journal, 18, 151-170.
4. Bowling, C., Cho, C. and Wright, D. (2004). Establishing a Continuum from Minimizing to Maximizing Bureaucrats: State Agency Head Preferences for Government Expansion - A Typology of Administrator Growth Postures, 1964-1998. Public Administration Review, 64, 489-499.
5. Bryson, J. (2004). Strategic Planning for Public and Nonprofit Organizations: a guide to strengthening and sustain organizational achievement, 3rd ed. San Francisco: Jossey-Bass.
6. Conybeare, J. (1984). Bureaucracy, Monopoly and Competition: A Critical Analysis of the Budget-Maximizing Model of Bureaucracy. American Journal of Political Science, 28, 479-502.
7. Davis, J., Eisenhardt, K., & Bingham, C. (2009). Optimal Structure, Market Dynamics, and the Strategy of Simple Rules. Administrative Science Quarterly, 54, 413-452.
8. Deming, W.E. (1994). The New Economics: For industry, government, education, 2nd ed. Cambridge, MA: MIT Press.
9. Dolan, J. (2000). The Budget-Minimizing Bureaucrat? Empirical Evidence from the Senior Executive Service. Public Administration Review,61, 42-50.
10. Downs, A. (1967). Inside Bureaucracy. Boston: Little, Brown and Company.
11. Fiske, S. & Taylor, S. (1984). Social Cognition. New York: Random House.
12. Gigerenzer, G. (2000). Adaptive Thinking: Rationality in the New World. New York: Oxford University Press.
13. Gigerenzer, G. (2008). Rationality for Mortals. New York: Oxford University Press.
14. Gigerenzer, G. (2014). Risk Savvy: How to Make Good Decisions. New York: Viking Penguin.
15. Gigerenzer, G. & Brighton, H. (2009). Homo Heuristicus: Why Biased Minds Make Better Inferences. Topics in Cognitive Science, 107–143.
16. Grossman, H. & Mendoza, J. (2003). Scarcity and Appropriative Competition. European Journal of Political Economy, 19, 747-758.
17. Holland, J. (1995). Hidden order: How adaptation builds complexity. Reading, MA: Helix Books.
18. Kahneman, D. & Tversky, A. (1979). Prospect Theory: An Analysis of Decisions Under Risk. Econometrics, 47, 263-291.
19. Kahneman, D. & Tversky, A. (1996). On the Reality of Cognitive Illusions. Psychological Review, 103, 582-591.
20. Kiel, L. D. (2005). A primer for agent-based modeling in public administration: Exploring complexity in “would-be” administrative worlds. Public Administration Quarterly, 29, 268-296.
21. Langton, C. (1989). Artificial life. In Artificial life, the proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, held September, 1987 in Los Alamos, ed. C.G. Langton, 1-47. Redwood City, CA: Addison Wesley.
22. Lewin, R., Parker, T. & Regine, B. (1998). Complexity Theory and the Organization: Beyond the Metaphor. Complexity, 3, 36-40.
23. Moynihan, D. (2013). Does Public Service Motivation Lead to Budget Maximization? Evidence from an Experiment. International Public Management Journal, 16, 179-196.
24. Niskanen, W. (1971). Bureaucracy and Representative Government. Chicago: Aldine.
25. North, M. & Macal, C. (2007). Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling. New York: Oxford University Press.
26. Oses-Eraso, N. & Vildrich-Grau, M. (2006). Appropriation and concern for resource scarcity in the commons: An experimental study. Ecological Economics, 63, 435-445.
27. Ostrom, E. (1999). Self-governance and forest resources. Occasional Paper, vol. 20. Center for
28. International Forestry Research, CIFOR.
29. Railsback, S. & Grimm, V. (2012). Agent-based and individual-based modeling: A practical introduction. Princeton University Press. New Jersy.
30. Shah, A., Mullainathan, S., & Shafir, E. (2012). Some Consequences of Having Too Little. Science, 338, 682-685.
31. Sull, D. & Eisenhardt, K. (2015). Simple Rules: How to Thrive in a Complex World. Boston: Houghton Mifflin Harcourt.
32. Wildavsky, A. (1964). The Politics of the Budgetary Process. Boston: Little, Brown.
33. Wilensky, U. (1998). NetLogo Wealth Distribution model. http://ccl.northwestern.edu/netlogo/models/WealthDistribution. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
34. Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.
This work is licensed under a Creative Commons Attribution 4.0 International License.