Experimental Economics: A Revolution in Understanding Behaviour
Bibliographic record
Abstract
What is the best compensation package to offer employees? How should choice among investments in pension plans be structured? Should a government use auctions to sell natural resources? Is it possible to design a market to reduce non-point source pollution in Quebec's watersheds? What holds people back from trying technologies that are completely new to them? Over the last two decades a revolution has occurred in the advancement of our ability to answer questions such as these. This revolution is called experimental economics. Experimental economics is the use of a controlled laboratory environment to understand decisions people make. In an economics experiment, people make decisions in a laboratory. They are paid according to the outcome of their decisions, and their decisions are analyzed to determine the effect of an institutional or environmental change that is being tested. Through the analysis of behaviour in controlled economics experiments, much has been learned about behaviour when outcomes are uncertain: for example, new notions about preferences toward risk and consumption over time have been developed. Much has also been learned about how people behave in strategic environments: for example, bidding behaviour in auctions is better understood, and the strategies people use as they learn how to trust each other have been observed. The purpose of this report is to describe the methodology of experimental economics and to detail its major uses. We will focus on the ability to measure behaviours in a wide variety of situations important to organizations. We will show, with examples from our own work, how feedback between the laboratory and the field can result in new understanding of decisions in an effort to affect the cycle of poverty in a developing country in fundamentally new ways. What is the best compensation package to offer employees? How should choice among investments in pension plans be structured? Should a government use auctions to sell natural resources? Is it possible to design a market to reduce non-point source pollution in Quebec's watersheds? What holds people back from trying technologies that are completely new to them? Over the last two decades a revolution has occurred in the advancement of our ability to answer questions such as these. This revolution is called experimental economics. Experimental economics is the use of a controlled laboratory environment to understand decisions people make. In an economics experiment, people make decisions in a laboratory. They are paid according to the outcome of their decisions, and their decisions are analyzed to determine the effect of an institutional or environmental change that is being tested. Through the analysis of behaviour in controlled economics experiments, much has been learned about behaviour when outcomes are uncertain: for example, new notions about preferences toward risk and consumption over time have been developed. Much has also been learned about how people behave in strategic environments: for example, bidding behaviour in auctions is better understood, and the strategies people use as they learn how to trust each other have been observed. The purpose of this report is to describe the methodology of experimental economics and to detail its major uses. We will focus on the ability to measure behaviours in a wide variety of situations important to organizations. We will show, with examples from our own work, how feedback between the laboratory and the field can result in new understanding of decisions in an effort to affect the cycle of poverty in a developing country in fundamentally new ways.
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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 | 0.041 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".