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I want to begin with a quotation from David Hume on trade. He was writing in the 18th century in Scotland. David Hume was part of what we call the Scottish Enlightenment, and the two most important figures in the Scottish Enlightenment were David Hume and Adam Smith. And this is David Hume on trade:

I want to begin with a couple of quotations. And in fact, here and there in the paper I will be using quotations from David Hume, Adam Smith or Hayek. This is not because I started as a classical economics scholar. It’s also not because I started with an interest in Friedrich Hayek, an Austrian economist. The special value of their contributions is what I discovered after having a long career in experimental economics. And in fact, without my experience as an experimentalist I don’t think there’s any way that I could have been able to appreciate the full significance of these quotations that I’m going to use. I now see them as enormously insightful in terms of what we have learned from experimental economics. What astonishes me is that Hayek and some of these 18th century scholars could have gotten to this level of understanding without doing experiments. I could not have done that.

Many people consider thatWard Edwards’ 1954 review paper marks the beginning of behavioral decision making or the study of how people make decisions. Fifty years after Edwards’ paper, it is illuminating to reflect on the progress of the field over the last five decades and to ask what the next fifty years might have in store. To do this, I identify ten major ideas or findings that have emerged to date. These are: (1) that judgment can be modeled; (2) bounded rationality; (3) to understand decision making, understanding tasks is more important than understanding people; (4) levels of aspiration or reference points and loss aversion; (5) heuristic rules; (6) adding and the importance of simple models; (7) the search for confirmation; (8) the evasive nature of risk perception; (9) the construction of preference; and (10) the roles of emotions, affect, and intuition. I further identify major challenges currently facing the field. These include linking knowledge to the growing body of work in neuroscience, developing methodologies that can generalize experimental results, having more impact on helping people make decisions, and extending collaboration with other disciplines in the social sciences.

Experimental research suggests that decision makers tend to overweight low probability (rare) events when they rely on a description of the possible outcomes (e.g., the situations addressed by Kahneman & Tversky [19]), but to underweight low probability events when they rely on personal experience (e.g., Barron & Erev [3]). The current chapter summarizes two lines of research designed to evaluate the implications of this pattern. The first line includes an experimental examination of the two contradicting effects. The results suggest that the two effects do not cancel each other. Rather, it is possible to predict which effect is likely to occur in a particular situation. The second line of research explores if the understanding of the experimental results can be used to derive practical implications. Four examples are presented that demonstrate that the experimental pattern can shed light on the economics of small decisions.

This paper describes central topics in our research program on social preferences. The discussion covers experimental designs that discriminate among alternative components of preferences such as unconditional altruism, positive reciprocity, trust (in positive reciprocity), negative reciprocity, and fear (of negative reciprocity). The paper describes experimental data on effects of social distance and decision context on reciprocal behavior and male vs. female and group vs. individual differences in reciprocity. The exposition includes experimental designs that provide direct tests of alternative models of social preferences and summarizes implications of data for the models. The discussion reviews models of other-regarding preferences that are and are not conditional on others revealed intentions and the implications of data for these models.

This paper addresses five questions about how stock market works and what we have learned from experiments in this field. 1, Why do we need even more data on financial market? Don’t we have enough already? 2. How could the data from such small scale simple markets help us gain insights into far more complex investment environments? 3. Is experimental finance a branch or variation of behavioral economics/behavioral finance? 4. What have we learned so far from assets market experiments? 5. What is next?

In the 1970s, experimental economics split from social psychology by embracing rational choice and equilibrium methods. Behavioral economics has recently narrowed the divide, to the dismay of some. The present paper argues that evolutionary dynamics provides a framework which unifies the best features of social psychology with equilibrium and rational choice.

Ongoing research in cheating in markets illustrates the main points. A new equilibrium model provides distinctive testable predictions under three regimes: autarky, frictionless free trade, and anonymous foreign trade with opportunities to cheat. The predictions organize quite well the data collected so far. Later phases of the project will allow trader networks to evolve, altering the market institution and perhaps affecting preferences. Thus the major forces recognized by social psychologists can be combined with a rationality and equilibrium to study how markets respond to the risk of cheating.

Recently, we find the rise of criticisms in the field of mechanism design, which is to design a mechanism or system for achieving social goals such as efficiency and equity in the distribution of goods and services.

First of all, there is a criticism from experimentalists. In the verification of various mechanisms using human subjects in laboratories, these designed mechanisms do not necessarily function as prescribed. This fact itself is a criticism to not only this particular field, but also extending to economics as a whole. Departing from the initial stage of surprise with why theoretically expected results cannot be observed in laboratories, we are now entering the stage of determining why they do not function and what are the essential factors involved.

Second criticism involves the presumptions in the theories themselves. Mechanism design has not paid sufficient attention to information exchanges between people, the cost of processing, and the selections of equilibrium concepts. What is questioned now is the real validity of frameworks themselves, on which the theories are nested.

This report is, through exploratory works on issues of mechanism design, to contemplate hints of new approaches to the questions: what it means to design a mechanism; how to design them; and what shall be the next step economics needs to aim for.

In the standard intertemporal specification of expected utility the coefficient of relative risk aversion is the reciprocal of the rate of intertemporal substitution 1/. This has been suspected as a source of poor performance of the standard stochastic consumption model and the risk-premium puzzle in asset pricing . More generally, the problematic feature of expected utility applied to intertemporal settings is that its treatment of ‘gambling over time’ cannot distinguish risk aversion and intertemporal substitution .

Informational cascades are said to occur if players ignore private signals by following an established pattern of actions that their predecessors have chosen as a result of Bayesian updating in a sequential decision-making problem. Anderson and Holt [1] confirmed that informational cascades certainly occur in the laboratory as Bikhchandani et al. [2] suggests. However, other experimental studies including Çelen and Kariv [3], Huck and Oechssler [4], Kraemer et al. [5], and Nöth and Weber [6] generally argue that subjects put more weight on their private sig-nals than the Bayesian model assumes.