Daniel Kahneman, Nobel Prize Laureate for Economics and Professor of Psychology at Princeton University, discusses how behavioural economics is likely to improve consumer protection in the United States and that policy makers in America recognise the value of this field of economic thought.
In the 21st century, the toolkit of the modern designer is rapidly expanding. Design practice is maturing, and what was once a focus on aesthetics and usability is broadening to incorporate interdisciplinary knowledge from a variety of fields like behavioral economics and cognitive psychology. These disciplines shed light on the factors that impact human decision-making and motivate our behaviors.
Display advertising is a multi-billion dollar industry that has traditionally used a pricing scheme based on the number of impressions delivered. The number of impressions of an ad is simply the number of downloads of that ad. One impression, however, does not diﬀerentiate between an ad that is in view for ﬁve seconds or ﬁve minutes. Since advertisers seek brand recognition and recall, we ask whether a time-based account- ing of advertising can better align with advertisers’ goals. This work aims to model the basic relationship between ad exposure time and the probability that a viewer will remem- ber an advertisement. We investigate this question via two behavioral experiments, conducted using Amazon Mechan- ical Turk, in which people viewed Web pages accompanied by ads. The amount of time the ads were in view was either determined endogenously (as a function of reading speed) or exogenously (as a function of a timer and random assign- ment). Our results suggest that for exposure times of up to one minute, there is a strong, causal inﬂuence of expo- sure time on ad recognition and recall, with the marginal eﬀects diminishing at durations beyond this level. Simple models describing memory response as a function of the log- arithm of exposure time provide a good ﬁt. In addition, we ﬁnd that advertisements that are displayed when the Web page loads attain greater marginal increases in recognition per unit time than do ads that come into view second in a sequence. Nonetheless, for both types of ads, exposure time has a substantial eﬀect. A psychologically-informed accounting system based on ad exposure duration, sequence and onset time may more closely align with advertiser goals than the industry standard of impression-based accounting.
Over the last decade or so, behavioral economics has fundamentally changed the way economists conceptualize the world. Behavioral economics is an umbrella of approaches that seek to extend the standard economics framework to account for relevant features of human behavior that are absent in the standard economics framework.2 Typically, this calls for borrowing from the neighboring social sciences, particularly from psychology and sociology. The emphasis is on well-documented empirical findings: at the core of behavioral economics is the conviction that making our model of an economic man more accurate will improve our understanding of economics, thereby making the discipline more useful.
It is natural for such an endeavor to begin as a subdiscipline—one that catalogs anomalies and explores alternative ways to model choice, with applications illustrating the workings of such models. A more ambitious role for behaviorally based insights is to effect how researchers in applied fields make both positive and normative analyses. By and large, this is the arena in which the usefulness of new ideas is eventually evaluated. In the long run, one expects the arguments, if useful, to be integrated into the mainstream literature.
Human nature is to reason in certainties. It takes training to rid yourself of that handicap. “I can live with doubt and uncertainty. I think it’s much more interesting than live with answers which might be wrong. I have approximate answers and different degrees of certainty about various things, but I’m not absolutely certain of anything.”
Perfectly rational decision-makers maximize expected utility, but crucially ignore the resource costs incurred when determining optimal actions. Here we propose an axiomatic framework for bounded rational decision-making based on a thermodynamic interpretation of resource costs as information costs. We show that this axiomatic framework enforces a unique conversion law between utility and information, which can be characterized by a variational “free utility” principle akin to thermodynamical free energy. This variational principle constitutes a normative criterion that trades oﬀ utility and information costs, the latter measured by the Kullback-Leibler deviation between a distribution representing a desired policy and a reference distribution representing an initial default policy. We show that bounded optimal control solutions can be derived from this variational principle, which leads in general to stochastic policies. Furthermore, we show that risk-sensitive and robust (minimax) control schemes fall out naturally from this framework if the environment is considered as an adversarial opponent. When resource costs are ignored, the maximum expected utility principle is recovered.
We outline, brie y, the role that issues of the nexus between noncomputability and unpredictability, on the one hand, and between undecidability and unsolvability, on the other, have played in Computable Economics. The mathematical underpinnings of Computable Economics are provided by (classical) recursion theory, varieties of computable and constructive analysis and aspectsof combinatorial optimization. The inspiration for this outline was provided by Professor Graca's thought-provoking recent article.
“Probably 99.999 percent of what goes on in the brain is automatic and unconscious. I have no idea what my next sentence will be, and sometimes I sound like it. (…) We think the other stuff, the ‘me,’ the ‘self,’ — we think that’s really important. We think there is somebody in charge —somebody pulling the levers. (…)
“The brain is automatic but people are free. You are responsible. Get over it.”
Free will is not a useful concept at the level of brain biology, to summarize Gazzaniga, because the biology is fixed. We cannot control our brains. It is at the level of interactions between people where concepts like responsibility and justice can be addressed. Gazzaniga compared the problem to an analysis of traffic, which cannot be achieved by studying individual cars. “Traffic only exists in the interaction,” he said.”
Behavioral economics uses evidence from psychology and other disciplines to create models of limits on rationality, willpower and self-interest, and explore their implications in economic aggregates. This paper reviews the basic themes of behavioral economics: Sensitivity of revealed preferences to descriptions of goods and procedures; generalizations of models of choice over risk, ambiguity, and time; fairness and reciprocity; non-Bayesian judgment; and stochastic equilibrium and learning. A central issue is what happens in equilibrium when agents are imperfect but heterogeneous; sometimes firms “repair” limits through sorting, but profit- maximizing firms can also exploit limits of consumers. Frontiers of research are careful formal theorizing about psychology and studies with field data. Neuroeconomics extends the psychological data use to inform heorizing to include details of neural circuitry. It is likely to support rational choice theory in some cases, to buttress behavioral economics in some cases, and to suggest different constructs as well.
What cognitive capabilities allow Homo sapiens to successfully bet on the stock market, to catch balls in baseball games, to accurately predict the outcomes of political elections, or to correctly decide whether a patient needs to be allocated to the coronary care unit? It is a widespread belief in psychology and beyond that complex judgment tasks require complex solutions. Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true: Humans do not need complex cognitive strategies to make good inferences, estimations, and other judgments; rather, it is the very simplicity and robustness of our cognitive repertoire that makes Homo sapiens a capable decision maker
In the 21st century, the toolkit of the modern designer is rapidly expanding. Design practice is maturing, and what was once a focus on aesthetics and usability is broadening to incorporate interdisciplinary knowledge from a variety of fields. The problems we solve are changing too - growing in size, scope, and complexity. We now find ourselves working in a wide range of domains, from education and policy development to energy consumption and healthcare. At the same time, it’s become increasingly apparent that whether we’re designing a mobile phone, a surgical release form, a corporate policy, or the infrastructure for a subway system, all of the design decisions we make have the potential to influence human behavior – whether we intend them to or not.
In recent decades, there has been a growth in economic research programs loosely described as behavioral economics. Despite calls for closer engagement between behavioral and Post Keynesian economics, the impact of behavioral economics on the Post Keynesian literature remains relatively limited. In this paper, we examine the nature of behavioral economics and the case made by those who claim or demonstrate that it can make a contribution to Post Keynesianism. We also consider why to date behavioral economics has had such a restricted effect. We conclude that there is scope for further successful engagement between behavioral economics and Post Keynesian economics if it is based on explicitly stated common ground, defined in terms of methodology.
This report was commissioned by the Office of Fair Trading (OFT) from London Economics in association with Steffen Huck and Jidong Zhou (University College London). It examines the implications of consumer behavioural biases for firms' decisions and hence for competitive equilibria. Consumer behavioural biases imply that consumers may not behave in the fully rational way that many economic models presume. What impact do these biases have on competition? Specifically, how does competition and pricing change when consumers are biased? Can inefficiencies that arise from consumer behavioural biases be mitigated by lowering barriers to entry? Do biased consumers make rational ones better or worse off? And will biased consumer behaviour be overcome through learning or education?
Jeremy Tremewan (Vienna) presented an interesting seminar on his research (with co-authors) into social preferences and strategic behaviour in the centipede game. Eliciting detailed information on beliefs about opponents' behaviour his research/experiment shows that contrary to many other findings subjects continue longer in the centipede game in outgroup scenarios vs ingroup scenarios. A prospective reference theory (Viscusi) model is used to help organise and understand this puzzling behaviour - with a bounded rationality explanation: subjects in outgroup treatments treat their beliefs as only partially informative and discount them more highly than do subjects in ingroup treatments (conditional on the PRT model being valid). A lively discussion with many interesting comments.
The problem of designing, managing, and coordinating the efforts of different parts of complexorganizations is central to the management and organizations literature. A central element, inturn, of Simon’s (1962) argument, which provides a foundation for understanding complexorganizations, is that the fundamental properties of complex systems are hierarchy and near-decomposability. These dual properties are argued to enhance the evolvability of such systems.A critical question, however, is whether boundedly rational managers will be able to identify anduncover some true, latent structure of hierarchy and decomposability. This question is intimatelyrelated to broader issues of concern to organization theory including the usefulness and value of design efforts and the implications of organizational change processes. In an effort to uniteSimon’s ideas about complexity with mainstream organization theory, we address three researchquestions: (1) how does the architecture or structure of complexity affect the feasibility andusefulness of boundedly rational design efforts; (2) do efforts to adapt in the space of organizational forms complicate or complement the effectiveness of first-order change efforts;(3) to what extent does the rate of environmental change nullify the usefulness of design efforts.We employ a computational model of organizational adaptation to examine these questions. Our results, in identifying the boundary conditions around successful design efforts, suggest that theunderlying architecture of complexity of organizations, particularly the presence of hierarchy, isa critical determinant of the feasibility and effectiveness of design efforts. We also find thatdesign efforts are generally complementary to efforts at local performance improvement andidentify specific contingencies that determine that extent of complementarity. We discuss theimplications of our findings for organization theory and design and also the burgeoning literatureon modularity in products and organizations.
Animals are proof that complete cognitive systems can be realized in neural substrates. It is thus natural that engineers from AI and machine learning have tried to design advanced cognitive systems on the basis of artificial neural networks. This has led to illuminating concepts and architectures in fields like computational linguistics, dynamic pattern recognition, autonomous agents, or evolutionary robotics. However, if one takes a close and critical look, one finds that nowhere do artificial systems close to biological levels of performance. One important cause for this gap is a lack of appropriate mathematical concepts. Biological neural systems are high-dimensional, nonlinear, heterogeneous, multiscale, nonstationary, stochastic, and heavily input-driven - a cocktail of properties which overwhelms current dynamical systems theory. Inasmuch as we do not possess mathematical models for such systems, we cannot understand them; and inasmuch as we do not understand, we cannot engineer. ...
...next time you miss a flight, are first in line after tickets sell out, or get stuck in traffic after trying out an alternative route home, just remember, your situation may be frustrating, but it’s not like you lost half a million dollars and it is not as if you will keep on remembering this for the rest of your life.