Abstract
Prior research suggests that the fit between task complexity and incentive schemes like pay-for-performance positively affects organizational performance. This study goes a step further and seeks to investigate how different types of pay-for-performance affect subordinates’ search behavior for novel solutions to complex decision problems. Based on NK fitness landscapes, the study employs an agent-based simulation with subordinate decision-makers individually adapting their search behavior via reinforcement learning. The results suggest that the emerging search strategy is subtly shaped by the (mis-)fit between task complexity and incentive structure. In particular, the results indicate that search behavior may arise for different “reasons” ranging from fostering new solutions to even preventing alterations.
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Notes
- 1.
For example, in the “standard” hidden action model of principal-agent theory, both contracting parties know the entire space of possible effort levels which the agent could employ [10]. Hence, particular search processes to discover new options within the solution space are not required [11, with further references].
- 2.
Unit head r remembers the compensation resulting from the status quo \(\mathbf {d}^{r*}_{t-1}\), and, from this, can infer its actual performance \({P}^{r}_{t}\) if the status quo is kept.
- 3.
\(0\le p^r(s^a,t) \le 1\) and \(\sum _{s^a\in S} (p^r(s^a,t))=1\) apply to probabilities \(p^r(s^a,t)\).
- 4.
Pretests indicated that the results do not principally change for a longer observation period. The similar holds for longer learning intervals (e.g., \(T^L=20\)); however, shortening the learning period notably below ten periods does not leave the search strategies “enough time” to unfold their particular potential.
- 5.
The term “cluster” is employed to indicate a group of simulation runs which show a similar combination of units’ search strategies in the last observation period. The term is not used in terms of a cluster analysis.
- 6.
An extensive analysis including average number and performance of local peaks of numerous interaction structures based on the NK framework is provided in [22].
References
Lawrence, P.R., Lorsch, J.W.: Differentiation and integration in complex organizations. Admin. Sci. Q. 12(1), 1–47 (1967)
March, J.G.: Exploration and exploitation in organizational learning. Organ. Sci. 2(1), 71–87 (1991)
Bushman, R.M., Indjejikian, R.J., Smith, A.: Aggregate performance measures in business unit manager compensation: The role of intrafirm interdependencies. J. Account. Res. 33(Sup.), 101–129 (1995)
Siggelkow, N., Rivkin, J.W.: Speed and search: Designing organizations for turbulence and complexity. Organ. Sci. 16, 101–122 (2005)
Wall, F.: Learning to incentivize in different modes of coordination. Adv. Complex Syst. 20(2–3), 1–29 (2017)
Manso, G.: Motivating innovation. J. Financ. 66, 1823–1860 (2011)
Lerner, J., Wulf, J.: Innovation and incentives: evidence from corporate R&D. Rev. Econ. Stat. 89, 634–644 (2007)
Davila, A., Foster, G., Li, M.: Reasons for management control systems adoption: Insights from product development systems choice by early-stage entrepreneurial companies. Account. Org. Soc. 34, 322–347 (2009)
Chenhall, R.H.: Developing an organizational perspective to management accounting. J. Manag. Account. Res. 24, 65–76 (2012)
Holmström, B.: Moral hazard and observability. Bell J. Econ. 10, 74–91 (1979)
Leitner, S., Wall, F.: Decision-facilitating information in hidden-action setups: an agent-based approach. J. Econ. Interact. Coor. 16, 323–358 (2021)
Chen, S.-H.: Varieties of agents in agent-based computational economics: a historical and an interdisciplinary perspective. J. Econ. Dyn. Control 36, 1–25 (2012)
Kauffman, S.A., Levin, S.: Towards a general theory of adaptive walks on rugged landscapes. J. Theor. Biol. 128, 11–45 (1987)
Kauffman, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, Oxford (1993)
Baumann, O., Schmidt, J., Stieglitz, N.: Effective search in rugged performance landscapes: a review and outlook. J. Manag. 45, 285–318 (2019)
Wall, F.: Agent-based modeling in managerial science: an illustrative survey and study. Rev. Manag. Sci. 10, 135–193 (2016)
Simon, H.A.: A behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955)
Simon, H.A.: Theories of decision-making in economics and behavioral science. Am. Econ. Rev. 49, 253–283 (1959)
Levitan, B., Kauffman, S.A.: Adaptive walks with noisy fitness measurements. Mol. Divers. 1, 53–68 (1995)
Brenner, T.: Agent learning representation: advice on modelling economic learning. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, pp. 895–947. Elsevier, Amsterdam (2006)
Lorscheid, I., Heine, B.-O., Meyer, M.: Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments. Comput. Math. Organ. Th. 18, 22–62 (2012)
Rivkin, J.W., Siggelkow, N.: Patterned interactions in complex systems: implications for exploration. Manag. Sci. 53, 1068–1085 (2007)
Redman, T.C.: The impact of poor data quality on the typical enterprise. Commun. ACM 41, 79–82 (1998)
Siggelkow, N., Rivkin, J.W.: When exploration backfires: unintended consequences of multilevel organizational search. Acad. Manag. J. 49, 779–795 (2006)
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Wall, F. (2022). Pay-for-Performance and Emerging Search Behavior: When Exploration Serves to Reduce Alterations. In: Czupryna, M., Kamiński, B. (eds) Advances in Social Simulation. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-92843-8_9
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