Experimental economics

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Experimental economics is the application of experimental methods [1] to study economic questions. Data collected in experiments are used to estimate effect size, test the validity of economic theories, and illuminate market mechanisms. Economic experiments usually use cash to motivate subjects, in order to mimic real-world incentives. Experiments are used to help understand how and why markets and other exchange systems function as they do. Experimental economics have also expanded to understand institutions and the law (experimental law and economics). [2]

Experiment Scientific procedure performed to validate a hypothesis.

An experiment is a procedure carried out to support, refute, or validate a hypothesis. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale, but always rely on repeatable procedure and logical analysis of the results. There also exists natural experimental studies.

Economic data or economic statistics are data describing an actual economy, past or present. These are typically found in time-series form, that is, covering more than one time period or in cross-sectional data in one time period. Data may also be collected from surveys of for example individuals and firms or aggregated to sectors and industries of a single economy or for the international economy. A collection of such data in table form comprises a data set.

In statistics, an effect size is a quantitative measure of the magnitude of a phenomenon. Examples of effect sizes are the correlation between two variables, the regression coefficient in a regression, the mean difference, or even the risk with which something happens, such as how many people survive after a heart attack for every one person that does not survive. For most types of effect size, a larger absolute value always indicates a stronger effect, with the main exception being if the effect size is an odds ratio. Effect sizes complement statistical hypothesis testing, and play an important role in power analyses, sample size planning, and in meta-analyses. They are the first item (magnitude) in the MAGIC criteria for evaluating the strength of a statistical claim. Especially in meta-analysis, where the purpose is to combine multiple effect sizes, the standard error (S.E.) of the effect size is of critical importance. The S.E. of the effect size is used to weigh effect sizes when combining studies, so that large studies are considered more important than small studies in the analysis. The S.E. of the effect size is calculated differently for each type of effect size, but generally only requires knowing the study's sample size (N), or the number of observations in each group.

Contents

A fundamental aspect of the subject is design of experiments. Experiments may be conducted in the field or in laboratory settings, whether of individual or group behavior. [3]

Design of experiments method and a specialization in statistics

The design of experiments is the design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation.

Experimental psychology refers to work done by those who apply experimental methods to psychological study and the processes that underlie it. Experimental psychologists employ human participants and animal subjects to study a great many topics, including sensation & perception, memory, cognition, learning, motivation, emotion; developmental processes, social psychology, and the neural substrates of all of these.

Social psychology is the scientific study of how people's thoughts, feelings, and behaviors are influenced by the actual, imagined or implied presence of others. In this definition, scientific refers to the empirical investigation using the scientific method. The terms thoughts, feelings, and behaviors refer to psychological variables that can be measured in humans. The statement that others' presence may be imagined or implied suggests that humans are malleable to social influences even when alone, such as when watching videos, sitting on the toilet, or quietly appreciating art. In such situations, people can be influenced to follow internalized cultural norms. Social psychologists typically explain human behavior as a result of the interaction of mental states and social situations.

Variants of the subject outside such formal confines include natural and quasi-natural experiments. [4]

A natural experiment is an empirical study in which individuals are exposed to the experimental and control conditions that are determined by nature or by other factors outside the control of the investigators. The process governing the exposures arguably resembles random assignment. Thus, natural experiments are observational studies and are not controlled in the traditional sense of a randomized experiment. Natural experiments are most useful when there has been a clearly defined exposure involving a well defined subpopulation such that changes in outcomes may be plausibly attributed to the exposure. In this sense, the difference between a natural experiment and a non-experimental observational study is that the former includes a comparison of conditions that pave the way for causal inference, but the latter does not.

Experimental topics

One can loosely classify economic experiments using the following topics:

Market (economics) Mechanisms whereby supply and demand confront each other and deals are made, involving places, processes and institutions in which exchanges occur.

A market is one of the many varieties of systems, institutions, procedures, social relations and infrastructures whereby parties engage in exchange. While parties may exchange goods and services by barter, most markets rely on sellers offering their goods or services in exchange for money from buyers. It can be said that a market is the process by which the prices of goods and services are established. Markets facilitate trade and enable the distribution and resource allocation in a society. Markets allow any trade-able item to be evaluated and priced. A market emerges more or less spontaneously or may be constructed deliberately by human interaction in order to enable the exchange of rights of services and goods. Markets generally supplant gift economies and are often held in place through rules and customs, such as a booth fee, competitive pricing, and source of goods for sale.

Game theory is the study of mathematical models of strategic interaction among rational decision-makers. It has applications in all fields of social science, as well as in logic, systems science, and computer science. Originally, it addressed zero-sum games, in which each participant's gains or losses are exactly balanced by those of the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

Evolutionary game theory (EGT) is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and analytics into which Darwinian competition can be modelled. It originated in 1973 with John Maynard Smith and George R. Price's formalisation of contests, analysed as strategies, and the mathematical criteria that can be used to predict the results of competing strategies.

Within economics education, one application involves experiments used in the teaching of economics. An alternative approach with experimental dimensions is agent-based computational modeling.

Economics education or economic education is a field within economics that focuses on two main themes: the current state of, and efforts to improve, the economics curriculum, materials and pedagogical techniques used to teach economics at all educational levels; and research into the effectiveness of alternative instructional techniques in economics, the level of economic literacy of various groups, and factors that influence the level of economic literacy. Economics education can be seen as a process, science and product; as a process - economics education involves a time phase of inculcating the needed skills and values on the learners, in other words, it entails the preparation of learners for would-be-economics educator (teachers) and disseminating of valuable economics information on learners in other for them to improve their standard of living by engaging in meaningful venture; as a science, it means that it is a body of organized knowledge which is subjected to scientific proves/test; and as a product, economics education involves the inculcation of saleable values/skills/disposition on the learners which are desirable by employers of labour and the society at large. Economics education is distinct from economics of education, which focuses on the economics of the institution of education.

A simulation game is "a game that contains a mixture of skill, chance, and strategy to simulate an aspect of reality, such as a stock exchange". Similarly, Finnish author Virpi Ruohomäki states that "a simulation game combines the features of a game with those of a simulation. A game is a simulation game if its rules refer to an empirical model of reality." A properly built simulation game used to teach or learn economics would closely follow the assumptions and rules of the theoretical models within this discipline.

Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information. Such rules could also be the result of optimization, realized through use of AI methods.

Coordination games

Coordination games are games with multiple pure strategy Nash equilibria. There are two general sets of questions that experimental economists typically ask when examining such games: (1) Can laboratory subjects coordinate, or learn to coordinate, on one of multiple equilibria, and if so are there general principles that can help predict which equilibrium is likely to be chosen? (2) Can laboratory subjects coordinate, or learn to coordinate, on the Pareto best equilibrium and if not, are there conditions or mechanisms which would help subjects coordinate on the Pareto best equilibrium? Deductive selection principles are those that allow predictions based on the properties of the game alone. Inductive selection principles are those that allow predictions based on characterizations of dynamics. Under some conditions at least groups of experimental subjects can coordinate even complex non-obvious asymmetric Pareto-best equilibria. This is even though all subjects decide simultaneously and independently without communication. The way by which this happens is not yet fully understood. [7]

In game theory, the Nash equilibrium, named after the mathematician John Forbes Nash Jr., is a proposed solution of a non-cooperative game involving two or more players in which each player is assumed to know the equilibrium strategies of the other players, and no player has anything to gain by changing only their own strategy.

Learning experiments

In games of two players or more, the subjects often form beliefs about what actions the other subjects are taking and these beliefs are updated over time. This is known as belief learning. Subjects also tend to make the same decisions that have rewarded them with high payoffs in the past. This is known as reinforcement learning.

Until the 1990s, simple adaptive models, such as Cournot competition or fictitious play, were generally used. In the mid-1990s, Alvin E. Roth and Ido Erev demonstrated that reinforcement learning can make useful predictions in experimental games [8] . In 1999, Colin Camerer and Teck-Hua Ho introduced Experience Weighted Attraction (EWA), a general model that incorporated reinforcement and belief learning, and shows that fictitious play is mathematically equivalent to generalized reinforcement, provided weights are placed on past history.

Criticisms of EWA include overfitting due to many parameters, lack of generality over games, and the possibility that the interpretation of EWA parameters may be difficult. Overfitting is addressed by estimating parameters on some of the experimental periods or experimental subjects and forecasting behavior in the remaining sample (if models are overfitting, these out-of-sample validation forecasts will be much less accurate than in-sample fits, which they generally are not). Generality in games is addressed by replacing fixed parameters with "self-tuning" functions of experience, allowing pseudo-parameters to change over the course of a game and to also vary systematically across games.

Modern experimental economists have done much notable work recently. Roberto Weber has raised issues of learning without feedback. David Cooper and John Kagel have investigated types of learning over similar strategies. Ido Erev and Greg Barron have looked at learning in cognitive strategies. Dale Stahl has characterized learning over decision making rules. Charles A. Holt has studied logit learning in different kinds of games, including games with multiple equilibria. Wilfred Amaldoss has looked at interesting applications of EWA in marketing. Amnon Rapoport, Jim Parco and Ryan Murphy have investigated reinforcement-based adaptive learning models in one of the most celebrated paradoxes in game theory known as the centipede game.

Market games

Edward Chamberlin is thought to have conducted "not only the first market experiment, but also the first economic experiment of any kind." [9] Vernon Smith, drawing on Chamberlin's work, but also modifying it in key respects, conducted pioneering economics experiments on the convergence of prices and quantities to their theoretical competitive equilibrium values in experimental markets. [9] Smith studied the behavior of "buyers" and "sellers", who are told how much they "value" a fictitious commodity and then are asked to competitively "bid" or "ask" on these commodities following the rules of various real world market institutions (e.g., the Double auction as well the English and Dutch auctions). Smith found that in some forms of centralized trading, prices and quantities traded in such markets converge on the values that would be predicted by the economic theory of perfect competition, despite the conditions not meeting many of the assumptions of perfect competition (large numbers, perfect information).

Over the years, Smith pioneered – along with other collaborators – the use of controlled laboratory experiments in economics, and established it as a legitimate tool in economics and other related fields. Charles Plott of the California Institute of Technology collaborated with Smith in the 1970s and pioneered experiments in political science, as well as using experiments to inform economic design or engineering to inform policies. In 2002, Smith was awarded (jointly with Daniel Kahneman) the Bank of Sweden Prize in Economic Sciences "for having established laboratory experiments as a tool in empirical economic analysis, especially in the study of alternative market mechanisms".

Finance

Experimental finance studies financial markets with the goals of establishing different market settings and environments to observe experimentally and analyze agents' behavior and the resulting characteristics of trading flows, information diffusion and aggregation, price setting mechanism and returns processes. Presently, researchers use simulation software to conduct their research.

For instance, experiments have manipulated information asymmetry about the holding value of a bond or a share on the pricing for those who don't have enough information, in order to study stock market bubbles.

Social preferences

The term "social preferences" refers to the concern (or lack thereof) that people have for each other's well-being, and it encompasses altruism, spitefulness, tastes for equality, and tastes for reciprocity. Experiments on social preferences generally study economic games including the dictator game, the ultimatum game, the trust game, the public goods game, and modifications to these canonical settings. As one example of results, ultimatum game experiments have shown that people are generally willing to sacrifice monetary rewards when offered low allocations, thus behaving inconsistently with simple models of self-interest. Economic experiments have measured how this deviation varies across cultures.

Contracts

Contract theory is concerned with providing incentives in situations in which some variables cannot be observed by all parties. Hence, contract theory is difficult to test in the field: If the researcher could verify the relevant variables, then the contractual parties could contract on these variables, hence any interesting contract-theoretic problem would disappear. Yet, in laboratory experiments it is possible to directly test contract-theoretic models. For instance, researchers have experimentally studied moral hazard theory, [10] adverse selection theory, [11] exclusive contracting, [12] deferred compensation, [13] the hold-up problem, [14] [15] flexible versus rigid contracts, [16] and models with endogenous information structures. [17]

Agent-based computational modeling

Agent-based computational modeling is a relatively recent method in economics with experimental dimensions. [18] Here the focus is on economic processes, including whole economies, as dynamic systems of interacting agents, an application of the complex adaptive systems paradigm. [19] The "agent" refers to "computational objects modeled as interacting according to rules," not real people. [18] Agents can represent social and/or physical entities. Starting from initial conditions determined by the modeler, an ACE model develops forward through time driven solely by agent interactions. [20] Issues include those common to experimental economics in general [21] and by comparison [22] as well as development of a common framework for empirical validation and resolving open questions in agent-based modeling. [23]

Methodology

Guidelines

Experimental economists generally adhere to the following methodological guidelines:

Critiques

The above guidelines have developed in large part to address two central critiques. Specifically, economics experiments are often challenged because of concerns about their "internal validity" and "external validity", for example, that they are not applicable models for many types of economic behavior, so the experiments simply aren't good enough to produce useful answers. However, none of the critiques towards this methodology are specific to it, as they are immediately applicable to either theoretical or empirical approaches or both. [24] [ citation needed ]

See also

Notes

  1. Including statistical, econometric, and computational. On the latter see Alvin E. Roth, 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, 70(4), pp. 1341–1378 Archived 2004-04-14 at the Wayback Machine .
  2. See, e.g., Grechenig, K., Nicklisch, A., & Thöni, C. (2010). Punishment despite reasonable doubt—a public goods experiment with sanctions under uncertainty. Journal of Empirical Legal Studies, 7(4), 847-867 (link).
  3. Vernon L. Smith, 2008a. "experimental methods in economics," The New Palgrave Dictionary of Economics , 2nd Edition, Abstract.
       • _____, 2008b. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
       • Relevant subcategories are found at the Journal of Economic Literature classification codes at JEL: C9.
  4. J. DiNardo, 2008. "natural experiments and quasi-natural experiments," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  5. • Vernon L. Smith, 1992. "Game Theory and Experimental Economics: Beginnings and Early Influences," in E. R. Weintraub, ed., Towards a History of Game Theory, pp. 241– 282.
       • _____, 2001. "Experimental Economics," International Encyclopedia of the Social & Behavioral Sciences , pp. 5100–5108. Abstract per sect. 1.1 & 2.1.
       • Charles R. Plott and Vernon L. Smith, ed., 2008. Handbook of Experimental Economics Results, v. 1, Elsevier, Part 4, Games, ch. 45–66 preview links.
       • Vincent P. Crawford, 1997. "Theory and Experiment in the Analysis of Strategic Interaction," in Advances in Economics and Econometrics: Theory and Applications, pp. 206–242. Cambridge. Reprinted in Colin F. Camerer et al., ed. (2003). Advances in Behavioral Economics, Princeton. 1986–2003 papers. Description, contents, and preview., Princeton, ch. 12.
  6. Martin Shubik, 2002. "Game Theory and Experimental Gaming," in Robert Aumann and Sergiu Hart, ed., Handbook of Game Theory with Economic Applications, Elsevier, v. 3, pp. 2327–2351. Abstract.
  7. Gunnthorsdottir Anna, Vragov Roumen, Seifert Stefan and Kevin McCabe 2010 "Near-efficient equilibria in contribution-based competitive grouping," Journal of Public Economics, 94, pp. 987-994.
  8. Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria,Ido Erev, Alvin E Roth,American economic review,1998/9/1,848-881
  9. 1 2 Ross Miller (2002). Paving Wall Street: experimental economics and the quest for the perfect market. New York: John Wiley & Sons. pp.  73–74. ISBN   978-0-471-12198-5.
  10. Hoppe, Eva I.; Schmitz, Patrick W. (2018). "Hidden action and outcome contractibility: An experimental test of moral hazard theory". Games and Economic Behavior. 109: 544–564. doi:10.1016/j.geb.2018.02.006. ISSN   0899-8256.
  11. Hoppe, Eva I.; Schmitz, Patrick W. (2015). "Do sellers offer menus of contracts to separate buyer types? An experimental test of adverse selection theory". Games and Economic Behavior. 89: 17–33. doi:10.1016/j.geb.2014.11.001. ISSN   0899-8256.
  12. Landeo, Claudia M.; Spier, Kathryn E. (2016). "Stipulated Damages as a Rent-Extraction Mechanism: Experimental Evidence". Journal of Institutional and Theoretical Economics JITE. 172 (2): 235–273. doi:10.1628/093245616x14534707121162. ISSN   0932-4569.
  13. Huck, Steffen; Seltzer, Andrew J; Wallace, Brian (2011). "Deferred Compensation in Multiperiod Labor Contracts: An Experimental Test of Lazear's Model". American Economic Review. 101 (2): 819–843. doi:10.1257/aer.101.2.819. ISSN   0002-8282.
  14. Hoppe, Eva I.; Schmitz, Patrick W. (2011). "Can contracts solve the hold-up problem? Experimental evidence". Games and Economic Behavior. 73 (1): 186–199. doi:10.1016/j.geb.2010.12.002. ISSN   0899-8256.
  15. Morita, Hodaka; Servátka, Maroš (2013). "Group identity and relation-specific investment: An experimental investigation". European Economic Review. 58: 95–109. CiteSeerX   10.1.1.189.3197 . doi:10.1016/j.euroecorev.2012.11.006. ISSN   0014-2921.
  16. Fehr, Ernst; Hart, Oliver; Zehnder, Christian (2014). "How do Informal Agreements and Revision Shape Contractual Reference Points". Journal of the European Economic Association. 13 (1): 1–28. doi:10.1111/jeea.12098. ISSN   1542-4766.
  17. Hoppe, Eva I.; Schmitz, Patrick W. (2013). "Contracting under Incomplete Information and Social Preferences: An Experimental Study". The Review of Economic Studies. 80 (4): 1516–1544. doi:10.1093/restud/rdt010. ISSN   0034-6527.
  18. 1 2 Scott E. Page, 2008. "agent-based models," The New Palgrave Dictionary of Economics , 2nd Edition. Abstract.
  19. Leigh Tesfatsion, 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," Information Sciences, 149(4), pp. 262–268. Abstract.
  20. Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, pp. 831–880. Abstract/outline. 2005 prepublication version.
      • Kenneth Judd, 2006. "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, v. 2, ch. 17, pp. 881– 893.
      • Leigh Tesfatsion and Kenneth Judd, ed., 2006. Handbook of Computational Economics, v. 2. Description Archived 2012-03-06 at the Wayback Machine & and chapter-preview links.
  21. Vernon L. Smith, 2008b. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  22. John Duffy, 2006. "Agent-Based Models and Human Subject Experiments," ch. 19, Handbook of Computational Economics, v.2, pp. 949–101. Abstract.
  23. • Leigh Tesfatsion, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, sect. 5. Abstract and pre-pub PDF.
       • Akira Namatame and Takao Terano (2002). "The Hare and the Tortoise: Cumulative Progress in Agent-based Simulation," in Agent-based Approaches in Economic and Social Complex Systems. pp. 3– 14, IOS Press. Description.
       • Giorgio Fagiolo, Alessio Moneta, and Paul Windrum, 2007 "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, 30(3), pp. 195–226.
  24. Camerer, Colin F. (2011-12-30). "The Promise and Success of Lab-Field Generalizability in Experimental Economics: A Critical Reply to Levitt and List". Working Paper Series.Cite journal requires |journal= (help)

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