Experiential Learning, Competitive Selection, and Downside Risk: A New Perspective on Managerial Risk Taking

Abstract

It is part of managerial wisdom that managers need to take risks in order to succeed. This is in stark contrast to the prominent agent-based theories of experiential learning and competitive selection that are known to be biased against risky alternatives in the long run. How can the positive managerial view on risk taking prevail given these results? Qualitative surveys of managers suggest risks to be acceptable if their outcome distribution meets certain criteria. We argue in this paper that these criteria are widely congruent with low downside risk. Using a novel simulation design, we analyze the effects of experiential learning and competitive selection on downside risk preferences in an ecology of agents. In the long run, experiential learning implies weak downside risk seeking, whereas competitive selection leads to strong downside risk aversion. Furthermore, competitive selection leads to the prevalence of outcome distributions with low downside risk in an ecology, even if they have a significantly higher level of total risk than comparable distributions with more downside risk. We draw implications for empirical studies on managerial behavior. The online appendix is available at https://doi.org/10.1287/orsc.2017.1149 .

On the learning patterns and adaptive behavior of terrorist organizations

“…Additionally, we find that terrorist organizations discount prior experience at the expense of more recent reinforcements. Contrary to common models of aspiration based learning (e.g., Jaspersen and Peter, 2017), we find no evidence that an aspiration level or reference point drives the behavior of terrorist organizations.…”

“…For the remainder of this analysis, we treat the terrorist organization as one homogeneous entity which makes decisions based on its entire past experience. While such an approach is common in organizational research (e.g., Herriott et al, 1985;Jaspersen and Peter, 2017), we are aware that organizations in general and terrorist organizations in particular are often not homogeneous (Neumann, 2016). In fact, it can even be rational for the planners of a terrorist organization to implement a heterogeneous decision making process which may ensure higher resilience towards counter-terrorism (Enders and Jindapon, 2010).…”

“…In the approach used here, the reinforcement of an action at time t, together with those reinforcements of all other actions, directly determines the choice probability of this action in period t. The alternative would be that the choice probability at time t is reached through updating the choice probability from the previous period, depending on the performance of last period's action. While this latter approach is not uncommon in organizational research (Jaspersen and Peter, 2017), we refrain from analyzing it here because it requires additional assumptions (see, e.g., Börgers and Sarin, 2000). We do, however, estimate such a model as a robustness check in Section 3.2.3.…”

Sensitivity analysis of agent-based models: a new protocol

“…The authors perform a robustness analysis on the size of the population and on several network parameters, showing that the path of the biofuel market share is similar for all distinct parameter values (the only exception is the case of very sparsely connected networks). Other examples of this use of sensitivity analysis can be found in Lenox et al (2006), Puranam and Swamy (2016), Jaspersen and Peter (2017), Keuschnigg and Ganser (2017), Clement and Puranam (2018), and Hernandez and Menon (2018).…”

The Aggregation–Learning Trade-off

“…By contrast, decision-making structures that are most effective in fostering individual learning leverage contrarians rather than marginalizing them: they allow contrarians to learn and allow other organizational members to learn from contrarians. The first stream, experiential learning, focuses on how performance feedback shapes beliefs about the merits of an agent's choice alternatives and subsequent actions (e.g., Levinthal and March 1981, Miner and Mezias 1996, Greve 2003, Reagans et al 2005, Denrell and Le Mens 2007, Argote and Miron-Spektor 2011, KC et al 2013, Jaspersen and Peter 2017, Sengul and Obloj 2017, Clough and Piezunka 2020). An individual learns by doing as the individual makes a choice, receives feedback on that choice, and updates beliefs about the merits of the alternatives based on feedback.…”