Learning to Incentivize in Different Modes of Coordination

Abstract

The paper studies which incentive systems emerge in organizations when self-interested managers collaboratively search for higher levels of organizational performance and the headquarters learn about the success of the incentive systems employed. The study uses an agent-based simulation and, in particular, controls for di®erent levels of intra-organizational complexity and modes of coordination, i.e., the way how preferences on the departmental site are aligned with each other in respect to the overall organizational objective. The results indicate that for di®erent levels of intra-organizational complexity di®erent incentive systems emerge: With lower intra-organizational complexity, in tendency, the less focus is put on¯rm performance and vice versa. However, results also suggest that the mode of coordination may considerably a®ect the emergence of the incentive structure. This provides support for the idea that multiple management controls, like the incentive system and the mode of coordination, should be regarded and designed as a system with interrelations among its components and not just as a collection of several control practices.

Modeling managerial search behavior based on Simon’s concept of satisficing

“…While hill-climbing algorithms are customary in computational studies capturing managerial search processes, it is worth mentioning that they often serve just as the nucleus: in many models, managerial search is embedded in a broader context. This context is, for example, defined by the incentive schemes shaping managers' objective functions and, thus, the particular "landscapes" managers are searching in (e.g., Siggelkow and Rivkin 2005;Wall 2017). Another contingency factor is the imprecision of managers' information, which may, accidentally, lead to short-term declines but long-term inclines of performance choices (Knudsen and Levinthal 2007;Wall 2016).…”

“…𝛽 r ⋅ (s r (t)) + (1 − 𝛽 r ) ⋅ s max,r (t) else (7) s max,r (t + 1) = ⌊s max,r (t + 1) + 0.5⌋ 2021) or team-based compensation (e.g., Siggelkow and Rivkin 2005;Wall 2017) which were incorporated in many agent-based models.…”

“…However, the "allowed" neighborhood of search could be broader than one, and also the number of alternatives the manager identifies could be higher than one. Both is often employed in models of organizational search (e.g., Siggelkow and Rivkin 2005;Wall 2017; for overviews Chang and Harrington 2006;Baumann et al 2019). If the number of alternatives s max,r identified providing a performance incline is higher than one, that option with the highest incline is selected (steepest ascent hill-climbing).…”

“…Computational models of managerial search often comprise adaptive processes based on experiential learning and backward-looking search behavior (e.g., Gavetti and Levinthal 2000;Kollman et al 2000;Dosi et al 2003;Ethiraj and Levinthal 2004;Siggelkow and Rivkin 2005;Wall 2017). In computational models of managerial search, for capturing experiential learning and backward-looking search behavior, hill-climbing algorithms prevail (for overviews see Ganco and Hoetker 2009;Puranam et al 2015;Baumann et al 2019).…”

Modeling Managerial Search Behavior based on Simon's Concept of Satisficing

“…Computational models of managerial search often comprise adaptive processes based on experiential learning and backward-looking search behavior (e.g., Gavetti and Levinthal 2000;Kollman et al 2000;Dosi et al 2003;Ethiraj and Levinthal 2004;Siggelkow and Rivkin 2005;Wall 2017). In computational models of managerial search, for capturing experiential learning and backward-looking search behavior, hill-climbing algorithms prevail (for overviews see Ganco and Hoetker 2009;Baumann et al 2019).…”