-$12.5.  Adding up the values for the relevant branches (using the assumed probabilities shown in Exhibit 2), the expected loss from taking protective action is:

(-$1000*0.0125)+(-100*0.2375)+(-1000*0.0075)+(-100*0.7425) = -$118, while the expected loss from inaction is: (-$900*0.15)+($0*0.10)+(-$900*0.0075)+($0*0.7425) = -$141.75, leading to a decision to take protective action. Obviously, this decision is quite sensitive to the subjective probabilities formed by the individual. If, for example, she assesses the probability of an outbreak as 0.2 or less, her decision switches to inaction. How people form subjective expectations under conditions of imperfect information and high stress then becomes crucial.

# **_Insights from Behavioral Economics and Survey Evidence_**

High uncertainty and pervasive lack of credible information is a common feature in the early stages of many infectious disease outbreaks, especially in developing countries. People are thrown back on seeking information from rumors, word of mouth or sensationalized media reporting, as was seen in Surat or in Guangdong during SARS. Under these circumstances it would not be surprising to learn of substantial biases in people’s subjective estimates of key probabilities, at least for a time. Research in behavioral economics over recent decades amply demonstrates the presence of such biases in probability judgments in many contexts.<sup>7</sup> Tversky and Kahneman (1974) note that, rather than undertake explicit probability calculations of the type illustrated in Exhibit 2, people generally form subjective probability judgments using “a limited number of heuristic principles which reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations.” These heuristics are generally quite useful but “sometimes they lead to severe and systematic errors.” The availability heuristic, for example, describes the tendency of people to assess the probability of an event according to the ease with which instances of the event can be brought to mind. There is then a tendency to seriously overweight events that are memorable, vivid in imagination or a source of fear or dread. People’s estimates of the probability of having an automobile accident shoots up temporarily after they see an overturned car by the roadside. Similarly, people tend to greatly overestimate the risk posed by an uncommon but dreaded event like a nuclear accident (compared to the estimates of experts or the actual frequencies of such events), while they underestimate the risks from more common sources such as x-rays. (Slovic and Weber, 2002).

Another source of error is the representativeness heuristic: people tend to form conditional probability judgments about the likelihood of a hypothesis (given some evidence) by how well the evidence resembles the hypothesis. They tend, however, to ignore the prior or base rate probability of the hypothesis in question, or the likelihood of the evidence arising for reasons other than the hypothesis (thus breaching the requirements of Bayes Rule for calculating conditional probabilities). People may then vastly overestimate the probability that a disease outbreak has occurred (H, the

> 7 See for example Tversky and Kahneman (1974), Rabin (1998), Mullainathan and Thaler (2000) and Camerer, Loewenstein and Rabin (2004) for surveys of this evidence. Frank (2004) looks at the contribution of behavioral economics to health economics.

13