Cognitive uncertainty
How do people make economic decisions under subjective uncertainty? In this paper, Enke and Graeber conduct a series of online experiments with a total of more than 3,000 participants eliciting controlled decisions about probabilities in the valuation of risky lotteries (choice under risk), inference from data (belief updating), and prediction of future events (economic forecasts). Some of their findings:
- “In choice under risk, more than 80% of all decisions are associated with strictly positive cognitive uncertainty, and this number rises to more than 90% in belief updating. Participants appear relatively consistent in their degree of cognitive uncertainty, both across repeated decisions within the same domain and across different decision domains.”
- Cognitive noise produces not only stronger compression of the average but also leads to larger average absolute distances to the normatively optimal decision.
Our results highlight that various judgment and decision errors that are traditionally viewed as distinct share, in fact, common cognitive origins: the noisy or heuristic decision-making people engage in when they find a problem too complex to solve precisely. This insight encourages further research aimed at tying together seemingly-distinct behavioral economics anomalies by focusing on the noise that is triggered by complexity.
Cognitive Uncertainty
Authors: Benjamin Enke, Thomas Graeber
From: Harvard University
Complexity of economic decisions
In this paper, Gabaix and Graeber propose an interesting way of looking at the complexity of economic decisions: “Thoughts and calculations underlying economic actions and beliefs are produced by the mind, akin to a cognitive factory.” Their cognitive production function uses cognitive labor as an input to generate an expected level of performance on a decision task. This cognitive production function is endogenized. Finally, they define two notions of complexity: a) input-based complexity which is the mental effort required to achieve performance level; and b) output-based complexity which is the performance shortfall – relative to perfect accuracy – of the response reached given mental effort.
We propose a simple, tractable theory of complexity for basic economic decision problems. The theory allows us to formalize intuitions about subjective perceptions of complexity. It provides tools to predict and measure complexity – e.g., the complexity of evaluating a cup of coffee, goods with complementarity, the tax system, lotteries, or the complexity of planning intertemporal consumption. Guided by the model, we then propose a simple and widely applicable experimental methodology to measure perceptions of complexity. Our experimental analysis confirms the core tenets of the theory in an intertemporal consumption task. We hope that our measure will be useful to predict and measure complexity and study its impact on economic decisions.
The Complexity of Economic Decisions
Authors: Xavier Gabaix, Thomas Graeber
From: Harvard University
