Experimental Economics: A Revolution in Understanding Behaviour
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,041 | 0,109 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,020 |
| Communication savante | 0,005 | 0,008 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».