to global change. If they make erroneous assumptions about how people use information or about what information they will want, they may misdirect their efforts, perhaps producing information no one needs, or producing information in a way that no one can use it (for instance, failing to provide credible estimates of uncertainty in their analyses, without which responsible action is impossible).

Analysts have often made erroneous assumptions of all three kinds. This is evident in the management of technologies with major environmental impacts, so it may also be true of the management of global change. For example, analysts and public officials have often erred in their attempts to anticipate, interpret, and manipulate lay people's responses to nuclear power stations, pesticides, and hazardous waste incinerators, acting without good information about what motivates or terrifies people about the hazards these technologies present (Fischhoff et al., 1981; Fischhoff and Furby, 1983; National Research Council, 1989b). Acting on such misconceptions can imperil major investments and social relations.

The human behaviors in question are the province of social science. Social scientists have some relevant knowledge and the best idea how predictable and malleable the behavior in question may be. Chapters 3 and 4 note many of the areas in which relevant knowledge may be found, as well as the limits of that knowledge. Social scientists can also contribute to the process of analyzing global change by advising natural scientists about the kinds of information about environmental systems that are needed for decision making. For example, the information usually generated by the soil science disciplines is not of the kind needed to analyze the economic effects of soil erosion; soil scientists could produce the information that economists and policy makers need, given input on the nature of that information.

In addition, social scientists have developed methods that may be useful for developing and validating natural science models of global change processes. For example, social scientists have developed mathematical techniques for comparing and combining imperfect indicators of the same underlying variable to produce more reliable indicators and increase understanding of the sources of disagreement (Bollen, 1980, 1989). Such methods can be employed by atmospheric scientists for estimating models built from scattered or imperfectly reliable data, such as on air pollutants or on relationships between industrial processes and emissions.

Thus, social science can help global change research by improving the inputs to models of the global environment, providing



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