George Wu

REVISION: Learning to Detect Change
Date Posted:Tue, 25 Nov 2014 23:27:00 -0600
People, across a wide range of personal and professional domains, need to detect change accurately. Previous research has documented systematic shortcomings in doing so, in particular, a pattern of over- and under-reaction to indications of change, resulting from a tendency to overweight signals of change at the expense of the environment that produces the signals. This investigation considers whether this pattern persists when participants are given the opportunity to learn. We find that the pattern of system neglect does persist, but that the impact of experience varies greatly across environments -- participants show reliable improvement in some conditions and virtually none in others. We explain this differential learning by formally characterizing environments in terms of the extent to which they: (i) provide consistent feedback; and (ii) tolerate non-optimal behavior. Whereas we find that learning is related to consistent feedback, the stronger -- and perhaps more surprising -- ...

REVISION: Incorporating Behavioral Anomalies in Strategic Models
Date Posted:Thu, 25 Jul 2013 14:23:47 -0500
Behavioral decision researchers have documented a number of anomalies that seem to run counter to established theories of consumer behavior from microeconomics that are often at the core of analytical models in marketing. A natural question therefore is how equilibrium behavior and strategies would change if models were to incorporate these anomalies in a consistent way. In this paper we identify several important and generalizable anomalies that modelers may want to incorporate in their models.

New: Comment on 'Obliquity' (by John Kay)
Date Posted:Thu, 11 Apr 2013 04:06:45 -0500
No abstract available.

Comment on 'Obliquity' (by John Kay)
Date Posted:Fri, 25 Jan 2013 09:29:35 -0600
This paper is a comment on Obliquity by John Kay which can be found at: http://ssrn.com/abstract=2206876.

New: Comment on 'Obliquity' (by John Kay)
Date Posted:Fri, 25 Jan 2013 04:29:35 -0600
No abstract available.

REVISION: Learning to Detect Change
Date Posted:Thu, 05 Feb 2009 11:06:54 -0600
People, across a wide range of personal and professional domains, need to detect change accurately. Previous research has documented systematic shortcomings in doing so, in particular, a pattern of over- and under-reaction to indications of change, resulting from a tendency to overweight signals of change at the expense of the environment that produces the signals. This investigation considers whether this pattern persists when participants are given the opportunity to learn. We find that the ...

REVISION: Learning to Detect Change
Date Posted:Mon, 02 Feb 2009 16:24:30 -0600
People, across a wide range of personal and professional domains, need to detect change accurately. Previous research has documented systematic shortcomings in doing so, in particular, a pattern of over- and under-reaction to indications of change, resulting from a tendency to overweight signals of change at the expense of the environment that produces the signals. This investigation considers whether this pattern persists when participants are given the opportunity to learn. We find that the ...

Learning to Detect Change
Date Posted:Mon, 02 Feb 2009 00:00:00 -0600
People, across a wide range of personal and professional domains, need to detect change accurately. Previous research has documented systematic shortcomings in doing so, in particular, a pattern of over- and under-reaction to indications of change, resulting from a tendency to overweight signals of change at the expense of the environment that produces the signals. This investigation considers whether this pattern persists when participants are given the opportunity to learn. We find that the pattern of system neglect does persist, but that the impact of experience varies greatly across environments -- participants show reliable improvement in some conditions and virtually none in others. We explain this differential learning by formally characterizing environments in terms of the extent to which they: (i) provide consistent feedback; and (ii) tolerate non-optimal behavior. Whereas we find that learning is related to consistent feedback, the stronger -- and perhaps more surprising -- finding is that more learning occurs in environments that are more tolerant of non-optimal behavior.

Detecting Regime Shifts: The Causes of Under- and Over-Reaction
Date Posted:Tue, 01 Apr 2008 07:32:02 -0500
Many decision makers operate in dynamic environments, in which markets, competitors, and technology change regularly. The ability to detect and respond to these regime shifts is critical for economic success. We conduct three experiments to test how effective individuals are at detecting such regime shifts. Specifically, we investigate when individuals are most likely to under-react to change and when they are most likely to over-react to it. We develop a system-neglect hypothesis: ...

Incorporating Behavioral Anomalies in Strategic Models
Date Posted:Mon, 11 Jun 2007 20:24:33 -0500
Behavioral decision researchers have documented a number of anomalies that seem to run counter to established theories of consumer behavior from microeconomics that are often at the core of analytical models in marketing. A natural question therefore is how equilibrium behavior and strategies would change if models were to incorporate these anomalies in a consistent way. In this paper we identify several important and generalizable anomalies that modelers may want to incorporate in their models.We briefly discuss each phenomenon, identify a key unresolved issue and outline a research agenda to be pursued.

REVISION: Incorporating Behavioral Anomalies in Strategic Models
Date Posted:Mon, 11 Jun 2007 09:20:36 -0500
Behavioral decision researchers have documented a number of anomalies that seem to run counter to established theories of consumer behavior from microeconomics that are often at the core of analytical models in marketing. A natural question therefore is how equilibrium behavior and strategies would change if models were to incorporate these anomalies in a consistent way. In this paper we identify several important and generalizable anomalies that modelers may want to incorporate in their models.

New: Claiming a Large Slice of a Small Pie: Asymmetric Disconfirmation in Negotiation
Date Posted:Fri, 10 Mar 2006 14:56:24 -0600
Three studies show that negotiators consistently underestimate the size of the bargaining zone in distributive negotiations (the small pie bias) and, by implication, overestimate the share of the surplus they claim (the large slice bias). We explain the results by asymmetric disconfirmation: Negotiators with initial estimates of their counterpart's reservation price that are "inside" the bargaining zone tend to behave consistently with these estimates, which become self-fulfilling, whereas ...

Claiming a Large Slice of a Small Pie: Asymmetric Disconfirmation in Negotiation
Date Posted:Fri, 10 Mar 2006 00:00:00 -0600
Three studies show that negotiators consistently underestimate the size of the bargaining zone in distributive negotiations (the small pie bias) and, by implication, overestimate the share of the surplus they claim (the large slice bias). We explain the results by asymmetric disconfirmation: Negotiators with initial estimates of their counterpart's reservation price that are "inside" the bargaining zone tend to behave consistently with these estimates, which become self-fulfilling, whereas negotiators with initial "outside" estimates revise their perceptions in the face of strong disconfirming evidence. Asymmetric disconfirmation can produce a population-level bias even when initial perceptions are accurate on average. We suggest that asymmetric disconfirmation has implications for confirmation bias and self-fulfilling prophecy research in social perception.

Detecting Regime Shifts: The Causes of Under- and Over-Reaction
Date Posted:Tue, 08 Jun 2004 00:00:00 -0500
Many decision makers operate in dynamic environments, in which markets, competitors, and technology change regularly. The ability to detect and respond to these regime shifts is critical for economic success. We conduct three experiments to test how effective individuals are at detecting such regime shifts. Specifically, we investigate when individuals are most likely to under-react to change and when they are most likely to over-react to it. We develop a system-neglect hypothesis: individuals react primarily to the signals they observe and secondarily to the environmental system that produced the signal. Three experiments, two involving probability estimation and one involving prediction, reveal a behavioral pattern consistent with our system-neglect hypothesis: under-reaction is most common in unstable environments with precise signals and over-reaction is most common in stable environments with noisy signals. We test this pattern formally in a statistical comparison of the Bayesian model with a parametric specification of the system-neglect model.