Between Inaction and Indecision

Enno Siemsen

Enno Siemsen

— 4 min read
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Between Inaction and Indecision

Every board gamer knows the problem. You play your favorite game with a 4 other people around the table; 4 of you make decisions reasonably fast. The remaining one takes a long time for their turn. In the meantime, the remaining players get bored by inaction. The slowest player is 'Herbie,' the bottleneck that keeps the game from progressing. Managing a game thus involves pacing players who suffer from indecision while addressing the boredom of those forced into inaction.

All faculty members running classroom simulations face this issue, magnified by the number of students in the room. Every time a single person is indecisive, the rest of the room waits - at first patiently, then more and more impatiently. In extreme cases, the instructor runs out of time during the simulation, and most students in the room become dissatisfied with their experience.

There is quite a bit of research on this problem. From the diffusion model of Ratcliff (1978) to the work of Kagan (1966) on reflection-impulsivity, the need for cognition by Cacciopo and Petty (1982), and the work on cognitive reflection by Frederick (2005), researchers have examined this tendency to take more time with decisions. I have actually written a paper about it as well (Moritz, Siemsen, and Kremer 2014). A few things are clear. The tendency to take more time is both a personality trait (some people simply take more time to deliberate than others) and situational (experts can identify when they can gain benefits from spending more time deliberating, and when they can instead make a decision more quickly, see https://doi.org/10.1111/cogs.70119). And in many situations, the decisions that take the longest are not necessarily the best ones. Claude gave me a good summary of the literature:

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Although they developed independently, these literatures can be brought together under a resource-rational (or bounded-optimality) view of deliberation, dating back to Simon (1955). On this view, deliberating is itself a choice: each increment of thought carries a cost — time, effort, opportunity — and an expected benefit, namely, a higher chance of making a better decision; a rational agent continues to deliberate only while the expected benefit exceeds the cost. The online-chess results (Russek et al., 2025; Sigman et al., 2010) show that human thinking time approximately tracks this calculus in the aggregate. Stable individual differences then arise naturally as differences in the terms of that implicit computation, and the disparate constructs reviewed above can be read as pointing at different terms. The cost a person assigns to time and mental effort is low for the high-NFC individual who finds the activity intrinsically rewarding (Cacioppo & Petty, 1982) and high for the impulsive responder. The value a person places on accuracy or optimality is elevated for the maximizer (Schwartz et al., 2002) and, in the formal language of evidence accumulation, is expressed as a wide decision boundary (Ratcliff & McKoon, 2008; Starns & Ratcliff, 2010). The disposition to notice that a fast first answer might be wrong and to override it is captured by cognitive reflection (Frederick, 2005) and conceptual tempo (Kagan, 1966). And the metacognitive stopping rule that determines when enough thinking has been done is the term that appears miscalibrated in trait indecisiveness, rumination, and intolerance of uncertainty (Frost & Shows, 1993; Hunter et al., 2021; Watkins & Roberts, 2020), where the agent fails to reach closure even as the marginal value of further thought falls toward zero.

This means that as a faculty member developing an app, you have to keep this behavioral phenomenon in mind. Here are a few principles in the design of the app that help manage the tension between indecision and inaction in the classroom:

  • Instructors should have visibility on who has submitted a decision and who has not submitted a decision in a particular turn.
  • Instructors should have the ability to nudge individual students who have not made a decision yet to speed up their decision-making.
  • Students who have made a decision should have the ability to review existing information and plan their next turn, so that the effect of idleness is less salient. Engage them during waiting time.
  • Students who have not made a decision yet should have visibility of how many other students in the room have already made their decision. They should recognize when they are the bottleneck and acutely feel the social pressure to speed up.
  • Instructors should be allowed to force a decision for a student. There should be a normal decision (like 'order what you ordered in the previous turn'), which is not necessarily the best, but is not a terrible decision either.
  • You can introduce a decision countdown timer. The amount of time available to students should be a parameter that instructors can set, and you should provide extra time to students during the initial rounds as they learn the game. The timer should be publicly announced. Students who do not submit a decision should default to the normal decision.

The decision countdown timer is draconian, but effective. It will raise stress levels among those who are less decisive and will likely lead to some criticism, with students pointing out that a short timer is unrealistic. I would avoid it if possible, but it is often a necessary evil. If you have a game with 30 turns, and you limit each decision to 2 minutes, you know that it will be over within an hour. This planning certainty is often necessary to plan out a classroom session.

References

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.

Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19(4), 25–42.

Frost, R. O., & Shows, D. L. (1993). The nature and measurement of compulsive indecisiveness. Behaviour Research and Therapy, 31(7), 683–692.

Hunter, L. E., Meer, E. A., Gillan, C. M., Hsu, M., & Daw, N. D. (2021). Increased and biased deliberation in social anxiety. Nature Human Behaviour, 6(1), 146–154.

Kagan, J. (1966). Reflection–impulsivity: The generality and dynamics of conceptual tempo. Journal of Abnormal Psychology, 71(1), 17–24.

Moritz, B., Siemsen, E., Kremer, M. (2014). Judgmental forecasting: Cognitive reflection and decision speed. Production and Operations Management, 23 (7), 1146-1160.

Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59–108.

Russek, E. M., Acosta-Kane, D., van Opheusden, B., Mattar, M. G., & Griffiths, T. L. (2025). Time spent thinking in online chess reflects the value of computation. Cognitive Science. https://doi.org/10.1111/cogs.70119

Schwartz, B., Ward, A., Monterosso, J., Lyubomirsky, S., White, K., & Lehman, D. R. (2002). Maximizing versus satisficing: Happiness is a matter of choice. Journal of Personality and Social Psychology, 83(5), 1178–1197.

Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.

Watkins, E. R., & Roberts, H. (2020). Reflecting on rumination: Consequences, causes, mechanisms and treatment of rumination. Behaviour Research and Therapy, 127, 103573.

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