A contingency theory of representational complexity in organizations

Paper highlights

Should a firm use a simple rule or a rich model of its environment? Three views give different answers. The simplicity view favors a few variables because simple representations are easier to learn and less vulnerable to noise. The complexity view favors richer representations because they are less likely to omit an important driver. The complexity-matching view often favors an intermediate representation whose detail matches the environment. This paper shows when each view is right.

The key distinction is between experience and informedness. Experience supplies observations from which a representation can be learned. Informedness tells the manager which variables and relationships are plausible enough to consider. A simple heuristic performs well only when it focuses on the right features.

Main results

• Simple representations work well under high outcome uncertainty, when managers know what matters but have limited experience, or when greater complexity is costly. Simplicity reduces estimation noise without discarding the important variables.
• Representations that match the environment’s complexity work best when managers are both informed and experienced. Knowledge points them toward the right variables, and experience lets them estimate a more detailed model reliably.
• Complex representations are usually safer when managers are uninformed. Without prior knowledge of what matters, a simple model may confidently omit the true drivers, whereas a richer model has a better chance of including them.
• The best complexity depends more on the firm’s experience and informedness than on the environment’s objective complexity.

Calls to “keep it simple” often assume that someone already knows which few variables deserve attention. That is a strong assumption in a new industry or after a radical change. Conversely, adding variables can lower accuracy when evidence is scarce or outcomes are highly noisy.

Why it matters

• Experience helps estimate relationships among selected variables; informedness helps select the right variables in the first place.
• Simplicity helps when it removes estimation noise while retaining relevant cues. It hurts when simplification removes those cues.
• The useful question is not only how complex the environment is, but whether decision-makers know which few features deserve to survive simplification.

How to use this paper

Cite this for

• A contingency theory of when simple, matching, or complex representations perform better.
• The distinction between experience with observations and informedness about which variables matter.
• Conditions under which heuristics outperform richer models and conditions under which complex representations are safer.

Useful for teaching

• Why “keep it simple” assumes someone knows which few cues deserve attention.
• How knowledge and data solve different parts of a representation problem.
• How uncertainty and noise can make a smaller model more accurate without making simplicity universally best.

Careful claim

Simple representations help when they retain relevant cues while reducing estimation noise; when decision-makers are uninformed about which cues matter, richer representations are often safer.

Abstract

A long-standing question in the organizations literature is whether firms are better-off by using simple or complex representations of their task environment.

We address this question by developing a formal model of how firm performance depends on the process by which firms learn and use representations. Building on ideas from cognitive science, our model conceptualizes this process in terms of how firms construct a representation of the environment and then use that representation when making decisions. Our model identifies the optimal level of representational complexity as a function of (a) the environment’s complexity and uncertainty and (b) the firm’s experience and knowledge about the environment’s deep structure. We use this model to delineate the conditions under which firms should use simple versus complex representations; in doing so, we provide a coherent framework that integrates previous conflicting results on which type of representation leaves firms better-off.

Among other results, we show that the optimal representational complexity generally depends more on the firm’s knowledge about the environment than it does on the environment’s actual complexity. We also show that the relative advantage of heuristics vis-à-vis more complex representations critically depends on an unstated assumption of “informedness”: that managers can know what are the most relevant variables to pay attention to. We show that when this assumption does not hold, complex representations are usually better than simpler ones.

Other links

• SSRN preprint

Last updated 2026-06-21