Hiring leaders: Inference and disagreement about the best person for the job
Bibliographic record
Abstract
Abstract Hiring is a critical determinant of organizational performance and has received considerable attention in economics where the focus is on identifying who is the best person for the job (an adverse selection problem) and ensuring that the person hired has incentives to behave in a desirable manner (a moral hazard problem). The implicit assumption in this literature is that everyone agrees on what constitutes the “best candidate.” In this paper we show that the economics literature fails to recognize that people will generally disagree over “what is best?” Answering this question requires people to make inferences about the environment the organization expects to experience in the future and to match this environment with leader characteristics. Given the idiosyncratic nature of inference, there will be disagreement on the “best person for the job,” even when everyone shares the same goals. The purpose of this paper is to outline why conflict regarding the most desirable person for the job emerges in rapidly changing environments and how this conflict is different from conflict that arises from self-interest and the presence of decision-making biases. The paper shows that conflict from inference, if properly dealt with, can actually improve decision-making, and what can be done to create the right conditions for this to occur. The paper also shows why hiring always involves an element of luck.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".