Abstract
Selection Logic claims are meaningful only if they can be tested. This article outlines a validation approach aligned with scientific norms: define scope, test internal coherence, and measure outcomes across repeated decisions.[^1]
1. Scope: where should we expect gains?
Selection Logic is most relevant when:
- stakes are medium/high,
- reversibility is low,
- information asymmetry is high,
- persuasion pressure is high.
2. Coherence: do theorems contradict?
The theory stack should remain consistent:
- axioms constrain claims,
- theorems derive from axioms,
- corollaries derive from theorems.
3. Outcomes: what to measure?
Practical outcome metrics:
- regret rate (self-report + behavior),
- need-consistency — Need consistency,
- fit score stability — Fit score,
- selection efficacy — Selection efficacy.
4. Falsifiability and iteration
A method that does not improve outcomes for a decision class should be revised or rejected—this is the point of A3 (improvability) and validation.[^1]
References
- Popper, K. R. (1959). The Logic of Scientific Discovery. Routledge. (Original work published 1935)[source]
- Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–18.[source]
- Berlin, I. (1969). Four Essays on Liberty. Oxford University Press.[source]
- Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of Consumer Research, 13(4), 411–54.[source]
- Ericsson, A., & Pool, R. (2016). Peak: Secrets from the New Science of Expertise. Houghton Mifflin Harcourt.[source]
- Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6), 995–006.[source]
- Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.[source]