Laboratory Experiments in Economics: Coming of Age Shyam Sunder, Yale University Barcelona LeeX Experimental Economics Summer School in Macroeconomics Universitat Pompeu Fabra Barcelona, June 14, 2016

A Discipline Grows Up Since Chamberlin reported the results of his classroom experiment in 1948, the acceptability, recognition, role, and methods of this sub-discipline have evolved Unlike 1970s and 80s, when editors of economics journals routinely rejected experimental papers as a deviant curiosity (our experience in the 1980s) Last year one issue of AER has more papers using experimental method than any other On the whole, the experimental method has grown beyond its “childhood” phase, is no longer “outside the tent” Being inside the tent brings responsibilities of “adulthood” for our sub-discipline? 11/15/2018 Design of Experiments

Responsibilities Identifying core concerns of the discipline on substantive, not just methodological grounds Contribution to core concerns of a discipline Constructive interchange with sister methodologies of the discipline Balance between advancement of the method and substantive knowledge about real phenomena Six special concerns 11/15/2018 Design of Experiments

Six Special Concerns Time Institutions Properties of institutions Is the experimenter a part of the game; subject expectations Lab results as the final word, and main source of research questions Risky curves Micromotives, macrobehavior (outcomes): emergence 11/15/2018 Design of Experiments

Core concerns of the discipline: substantive, not just methodological Disciplines get sterile when methods take the front seat, obscuring its classic or newly-identified substantive questions While methodological development is necessary part of a healthy discipline, the dominant concern must still be with a better understanding of the world where we live What proportion of the effort of the discipline goes into research about questions about the world (external references; substantive), and questions about research itself (internal references; methodological)? A simple test: try explaining my research question (and results) to my parents, spouse, or siblings; and assess if they appreciate my contributions to human civilization What if they don‘t? Whose problem is that? 11/15/2018 Design of Experiments

Contribution to core concerns of a discipline Where do we look for questions to address? On the street, news, and observation of the world Questions arising in the classroom (by students as well as in our own minds) that we cannot answer to our satisfaction Unresolved (perhaps abandoned) puzzles of the discipline Incremental variations on recent publications Replications: training wheels? Sacrifice for science Proving your advisor or academic god-parent right Identifying core concerns of economics that could not be addressed without experiments What are the core concerns of economics and sister disciplines such as psychology? Do we need to distinguish among them? Does making a distinction mean un/willingness to learn from others? 11/15/2018 Design of Experiments

Constructive interchange with sister methodologies Contribution of experimental method will also depend on how well we are able to take advantage of constructive interchange with sister methodologies of economics For example, economic theory and mathematical modeling What can be a constructive and mutually productive relationship between theory and experiments? 11/15/2018 Design of Experiments

Assumptions Purpose of building models is to gain a better understanding of some real phenomena of interest (what is the principal? My model or the world? Real phenomena are complex (perhaps infinitely detailed), rarely possible to understand/characterize them completely Theory identifies one or a few critical variables to gain a satisfactory (not perfect) understanding of the phenomenon of interest Theories are neither wrong nor right; some are more helpful than others in gaining insight and useful for making predictions Compare theories on basis of their help in understanding/predicting the real phenomena 11/15/2018 Design of Experiments

Infinite Detail: Fractals  11/15/2018 Design of Experiments

Nature of Theory Essence of theory is its simplicity Simplification by abstraction from details of real phenomena Assumptions perform the function of discarding the mass of detail Key assumptions and assumptions of convenience Lack of correspondence between assumptions of convenience and reality is the essence of a theory, and not a defect of theory (no assumptions, no theory) What about “unrealistic” assumptions? 11/15/2018 Design of Experiments

Meaning of an Empirical Test of a Theory? Theory is to real phenomena what a drawing or stick figure is to human body, or map to earth surface Correspondence is crude, but captures some essential feature(s) Model identifies some tautologies which are necessarily true when assumptions hold (unless there are errors in derivation) What does it mean to empirically test a theory? 11/15/2018 Design of Experiments

Single Theory Experiments Only one interesting theory is available for the phenomenon of interest “Test” is an assessment of robustness of the theory to deviations from assumptions of convenience If data are gathered from an environment that corresponds exactly to the assumptions of the theory, we should expect no deviations (if we do observe deviations, either the theory has error or the correspondence is missing) But empirical test is a costly method of discovering errors of derivation 11/15/2018 Design of Experiments

Creating Theory in Lab Exact correspondence to theory in the field or lab is not easy Even if we succeed in creating such correspondence, little could be learned from it except about presence of errors of derivation or lack of correspondence Error in derivation or lab environment or data collection Little useful scientific inference is possible from such mutual lack of correspondence 11/15/2018 Design of Experiments

Scientific Value of A Single-theory Empirical Test Assessment of how robustly the predictions of the theory correspond to data as the environment of data becomes less similar to the convenience assumptions of the theory See Figure 1. A is not robust, B is highly robust, and C lies in between. Under all three cases, the model is literally true (when all its assumptions hold). However, as the environment deviates from the strict assumptions, A’s predictive power declines sharply 11/15/2018 Design of Experiments

Single Theory Experiment 11/15/2018 Design of Experiments

How Do We Identify Key the Assumptions? Distinguishing between model and theory Model is a (“stick figure”) logical structure; a theory uses the model to suggest some statements about the real phenomena of interest Think of the real phenomena that motivates the model and the theory Ask which assumptions of the model are intended to limit the real environments sought to be understood Number of states and agents, preferences, probability distributions tend to be assumptions of convenience 11/15/2018 Design of Experiments

Design of Robustness Experiments Conduct a series of experiments, all holding the key assumptions, and progressively relaxing the convenience assumptions (e.g., the number of states) Conduct a series of experiments progressively increasing the number of alternative choices available to subjects (increasing the number of possible outcomes) If model predictions are supported by data when more alternatives are available, result is more robust; e.g.,Vernon Smith (1962) 11/15/2018 Design of Experiments

Fig. 3: Single Theory Experiment Smith (1962) 11/15/2018 Design of Experiments

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Fig. 4: Multi-Theory Experiment, Plott and Sunder (1982) 11/15/2018 Design of Experiments

1. Time Most economic models include time dimension (usually denoted by symbol t Few models specify what t represents in real terms—seconds, hours, days, years, or generations Presumably, such theories are so general that they holds for all interpretations of the time interval in real units Lab experiments could be a way of finding the appropriate interpretations of time in specific theories, in case they exist, and thus make a significant contribution of economic theory Also paying atttention to the real time dimension in design and conduct of experiments 11/15/2018 Design of Experiments

2. Institutions Experimental methods have highlighted the importance of economic institutions, their properties, and their evolution over time However, study of institutions in lab presents a special challenge Most individual decisions involve choice of a point on a function, but institutions being functions themselves, examination of their evolution calls for choices from a set of functions Choice on a function and of a function call for very different cognitive skills, experience, and time, and are difficult to study in the few hours of a typical session 11/15/2018 Design of Experiments

3. Properties of Institutions Experiments have been employed to identify the properties of institutions Real life institutions have great deal of detail, and thus can be simplified for laboratory use in thousands of ways When we try to use experiments to identify institutional properties, how do we choose which implementation of the institution in the lab is appropriate? Which implementation of double auction is „right“ or „better“? 11/15/2018 Design of Experiments

4. Experimenter as a part of the game What are the boundaries of the game we hypothesize the subjects to be playing? What do we know about the expectations subjects bring to the lab? What, if any, control can we exercise on their expectations? Is experimenter inside that boundary or outside? How do we keep ourselves outside the boundary? Is it enough to tell them that the experimenter is outside the boundary? How often is it true? 11/15/2018 Design of Experiments

5. Lab results as the final word? When can we stop with the lab results, convinced that we have a good understanding of the phenomenon of interest? When do we need to follow up the lab results with data from the field? 11/15/2018 Design of Experiments

Empirical Failure of EU 6. Risky Curves D. Bernoulli (1738) ---Von Neumann Morgenstern (1943): curved utility (Bernoulli) functions to understand choice under risk combined with dispersion of outcomes as risk This idea (EUT) is widely accepted in the field; theorists devise new parameterized curves (e.g., CPT); experimenters devise protocols to elicit data and estimate the parameters Meager empirical harvest: little stability in parameters outside the fitted context; power to predict out of sample poor-to-nonexistent; no convincing victories over naïve alternatives; surprisingly little insight into phenomena outside the lab (insurance, security, labor, forex markets, gambling, business cycles, etc.) Very quick reviews (research through 1960; measuring individual risk preferences; aggregate level evidence from the field) Raise doubts; not sure of way forward, some possibilities Alternative meanings/measures of risk Looking for explanatory power in decision makers’ opportunity sets, real options, and net pay-offs, instead of in unobserved curved Bernoulli functions Current work in evolution, learning theory, neuroeconomics, and physiology Empirical Failure of EU

Micromotives, Macro-outcomes (Emergence) Properties of individual elements of a system Properties of the larger (aggregate level) systems Deriving the latter from the former is not always possible: emergence, complexity A possible line between micro and macroeconomics: emergence Unity of Science movement and reductionism 11/15/2018 Design of Experiments

Fundamental Principle of Research Designs (after Einstein) Research design should be as simple as possible, but no simpler. 11/15/2018 Design of Experiments

Research Question What question do you wish to answer with your research? A question is one sentence with a question mark at the end (?). It should be a question whose answer you would like to know, but do not know After asking your friends, if you are the only one who does not know, think again, unless you have reasons to disagree with them What might be the possible answers to the question? How could one distinguish what is a better answer? 11/15/2018 Design of Experiments

Macro-Examples Robert Lucas and Edward Prescott What is the question? Why Experiment? What is essential? What is not essential? Robustness check 11/15/2018 Design of Experiments

Thank You. Shyam.sunder@yale.edu http://faculty.som.yale.edu/shyamsunder/research.html 11/15/2018 Design of Experiments