David H. Krantz - US grants

In science, in the law, in business, and in everyday social interaction, people attempt to base their beliefs on the available evidence. The standard (Bayesian) theory asserts that people's beliefs consist of probabilities for various propositions, for example, the probability that a particular scientific hypothesis is correct or the probability that an accused person is guilty as charged. But proposed applications of the Bayesian theory in science and in law have encountered strong and quite justifiable resistance, largely because the theory demands that people express detailed beliefs even in the absence of any evidence (prior probabilities). This research will develop non-Bayesian theories in which perceived strength of evidence will be measured on a numerical scale quite different from probability. This measurement system is well adapted to science and the law since, in the absence of evidence, all beliefs can be rated as zero. One main research activity will be to determine formulae for converting probabilistic and statistical information to the strength-of-evidence measurement scale, and thus will provide a basis for systematic integration of such information with other kinds of evidence. Another research activity will be to develop graded standards of statistical and nonstatistical evidence, against which arbitrary items of evidence can be compared and evaluated, much as a meterstick provides a graded series of lengths, against which the lengths of other objects can be measured. The graded standards for evidence will be most useful if they can be applied analytically, decomposing a complex body of evidence into parts, each of which can be evaluated by comparison with the standards. One of the principal research activities will be to evaluate the bias inherent in such analytic decompositions. That research will compare evaluations of partial evidence by two groups of people, those who are aware of the full body of evidence and those who are not. If possible, conditions will be found for which these two groups evaluate evidence identically, i.e., there is no appreciable bias due to parts of the evidence that are ignored in any step of the analytic process. The research will also test conditions under which strong biases are expected.

Although some decisions are made on the basis of simple direct
evaluation of "gut" feelings, or the utilities of the various
alternatives, many decisions involve conflicting feelings; and the
conflict-resolution process often takes into account evidence about
some uncertain events.

A typical example would be the conflict faced by a college student who
has an important exam the next day but wants to quit studying to
spend some time with friends. In the process of resolving this
conflict, many students consider the uncertain event that they will do
well in the exam even if they stop studying now. Evidence relevant to
this event includes how difficult the exam is expected to be and how
much preparation the student has done all semester in this course. If
the exam is only somewhat important, and the evidence leads the student
to feel pretty sure of doing well without further studying, the student
will probably decide to quit studying. Even if the exam is highly
important, a student who is very sure of doing well will probably
quit studying.

For some decisions, part of the evidence consists of estimated
probabilities derived from scientific models. A physician may
consider estimated probabilities of recurrence of cancer before
recommending therapy, or a homeowner may take account of earthquake
probabilities in deciding whether to reinforce the foundation.

The present research is based on a recently developed theory of
decision-making under uncertainty. The core idea is that people
categorize uncertain events (e.g., "pretty sure" or "very sure"
of doing well on the exam), and that such categorization is based on
various kinds of evidence (sometimes including probabilities
estimated from scientific models).

The research has two main branches. One focusses on decision
situations involving conflict. We study how decision rules change,
depending on value of goals (for example, a student may use a
"pretty sure" rule for a somewhat important exam, but a "very sure"
rule for a highly important one) and how evidence is used to
categorize the uncertain events. Our preliminary findings suggest
that different evidence weightings are used for different abstract
categories of uncertainty. This part of the research also examines
people's justifications for their decisions, in order to understand
better how justifications relate to actual decision rules underlying
choices.

The second branch of the research focusses on evidence judgment, in
isolation from the ultimate decision--for example, the judgments
that a detective or a scientist might use in forming or confirming
hypotheses. We are interested in the difference between initially
generating a hypothesis and confirming it; in how people react to
conflicting evidence; in how people use base rates and relative
likelihoods as evidence; and in the process of selecting relevant
evidence when it is obscured by irrelevancies.

To summarize, the major goals of the research include testing and
developing a rather new theory of decision making; studying how
scientific probability estimates can be used in policy decisions;
and delineating the differences between exploratory use of evidence
in formulating hypotheses versus hypothesis confirmation.

Seismologists have predicted that a severe earthquake in the
Istanbul region is highly likely in the next 30 years. This
forecast has drawn much attention in the media and raises
challenges as to how potential losses may be reduced by
protective measures. Decisions made by private individuals,
by officials, and by engineers and builders are all relevant
to this question. The principles underlying protective decision
making need to be better understood, for the sake of this and
many other applications.

We investigate how people determine whether to purchase insurance
and/or invest in loss-reduction measures against risks from fire, theft,
accidents or natural disasters. We examine the effects of different
ways in which risks can be communicated and different frames in which
information and choices can be presented; we also examine how
protective decisions are affected by instructional modules that aid
understanding of probability and that suggest useful general strategies
for framing decisions. To estimate these effects, controlled laboratory
experiments, field surveys and interviews will be conducted both in
the United States and in Turkey.

The Center for Research on Environmental Decisions (CRED) at Columbia University will coordinate a series of studies of decision processes underlying human adaptation to uncertainty and change-in particular climate-related uncertainty and climate change. The Center's mission is to address decisions made at multiple scales: by individuals, by small groups, and by organizations that face climate-related problems. Center research will be conducted by Columbia students and faculty affiliated with the Institute for Social and Economic Research and Policy (ISERP) and with various units of the Earth Institute (EI), a consortium of natural and social scientists and engineers committed to improving our understanding of the Earth, its environment and climate, as well as by students and faculty at six partner institutions (Bard College, University of California at Davis, University of Georgia, University of Miami, University of Pittsburgh, and University of Oregon). CRED research will (a) extend insights about the constructive nature of individual decision making to the context of group decisions, (b) integrate social motives more fully into theories of decision making, and (c) study individual and group processes in the laboratory and in field settings as climate-change and climate-variability related decisions occur. A carefully designed set of four laboratory projects, four historical and theoretical projects, and eight field projects around the world will provide interdisciplinary and complementary insights on five substantive objectives: (1) understanding the nature and impact of mental representations and framing in both individual and group climate decision settings; (2) understanding the role in decision making of affective, experiential information vs. analytic, statistical information; (3) understanding the effects of individual and group goals, group composition, and group processes in climate decisions; (4) improving the presentation format and delivery of probabilistic climate information; (5) developing microeconomic theories that incorporate knowledge gained about individual and group decision processes making and macroeconomic theories that integrate climate models and their impacts. Five field projects examine the use of seasonal climate forecasts in individual, group, and institutional decision processes. Three field projects deal with long-term climate change and examine the role of direct personal experience vs. statistical information in detecting and responding to climate change.

CRED's research contributions are only the first of four areas of contribution. Research results will feed directly into the design and testing of educational interventions, decision tools, and institutional strategies that will help people, organizations, and governments to better understand the risks and possible benefits associated with climate change and variability and the response options they have. Regarding education and training, the Center will work with several educational programs under the aegis of the Earth Institute at Columbia, Columbia's Teachers College, local New York colleges and high schools, and the Office of Minority Affairs in the Columbia Graduate School of Arts and Sciences. The Center's curriculum and decision tools development will utilize the expertise of Columbia's Center for New Media Teaching and Learning, and the Center for International Earth Science Information Network (CIESIN). The large and dense contact network of the International Research Institute for Climate Prediction (IRI) will facilitate dissemination of these products. In addition, the Center will work with the Weather Channel to produce educational segments on climate change and climate variability that will allow for audience feedback. This award was supported as part of the Fiscal Year 2003 Human and Social Dynamics priority area special competition on Decision Making Under Uncertainty (DMUU).

Delayed benefits are usually valued less than immediate ones, for several reasons. Such a reduction in value can be viewed as the result of applying a temporal discount factor. There have been many studies of the observed discounting of future receipts of money. For example, if a decision maker judges that $100, to be received in 2 years, is equal in value to $70 right now, the observed discount rate for this decision is about 30% for 2 years (or about 16% per year). However, people also pursue goals other than money, including good health, safety, leisure, belonging, status, well being of others, and a stable and attractive environment. Such non-monetary goals may also be valued less if their achievement is delayed, so the concept of a temporal discount factor can be extended to these other goals. Different discount factors may apply for different goal categories, and this, in turn, would affect how people make tradeoffs among goals attained at different points in time. For example, if a decision maker judges an environmental improvement 2 years hence to be almost as valuable as the same improvement achieved now, the discount rate might be only 3% per year for the environmental goal even though it is 16% per year for a monetary gain of $100. A person who is unwilling to give up $100 now, for immediate attainment of the environmental goal, might be willing to make a binding commitment to give up $100 with a delay of one year (worth only $84), in order to achieve the same environmental goal with a delay of 1 year. Thus, it is important to know how human temporal discounting varies across goal categories in order to analyze the long-term benefits of public policies relating to health, safety, and environment.

Our previous research showed the importance of affiliation and social goals (such as status or adherence to group norms) for decisions concerning environmental goals. Our new research will determine the best ways of measuring temporal discount factors for different goal categories, and will then quantitatively compare discount rates for a variety of economic, social and environmental goals. We focus both on typical responses and on individual and cultural variation. The research will advance the theory of decision making in social and group contexts (temporal discounting of social goals has not previously been considered), will develop methods for assessing temporal discount factors, and will lead to better analysis of public policy costs and benefits. For example, we will assess discount rates for some costs and benefits of measures to prevent global warming, using a diverse national sample, to inform policy on this critical issue. A field study will test applicability of our findings in an international population and will provide data on effective means of encouraging energy conservation.

For the first time in human history, more than 50 percent of the world's population resides in urban centers. By the year 2015, this fraction is expected to rise by more than 10 percent. The increasing expansion of urban centers is significantly impacting ecosystem services and the human communities dependent on these services, not least because the biogeophysical environments of urban regions and their surrounds are substantially different from those of rural areas. The effects of this difference can be seen in modified water cycles and climates within urban centers and exurban areas, amended urban soil properties and ecosystem species, and polluted urban airways and waterways. In many cases, degraded urban biogeophysical environments also are contributing to severe health problems among urban populations. Concern about the major ecological impact of urbanization has prompted the development of numerous strategies for improving ecological services within cities, many of which are focused on the preservation and/or recreation of natural landscape features. One strategy that is gaining rapid attention in the U.S. is the concept of an urban Green Area Factor (GAF) program. The goal of a GAF program is to provide a cost-effective, decentralized approach to the restoration or expansion of ecosystem services in urban environments, by setting targets for the percentage of "greening" to occur in the development of different parcels of urban land. The use of the GAF as an urban planning tool has multiple potential benefits from the perspectives of improving urban ecology and health, improving the aesthetics and habitability of urban environments, and engaging urban stakeholders in strategies for sustainable development. But despite its potential, scientific linkages between GAF guidelines and ecological outcomes remain nascent. Furthermore, information about U.S. public willingness to provide support and stewardship for various GAF strategies is limited. The goal of this research project is to conduct interdisciplinary research on the dynamic interactions between people, natural ecosystems, and green technologies in the dense urban environment of New York City. The investigators will build on their own prior research as well as partnerships with diverse local community groups and practitioners to monitor and quantify the ecological and public health benefits of natural ecosystems and evolving green technology interventions in three New York City neighborhoods; to establish the acceptance and value of such systems and interventions to stakeholders; and to develop a GAF-based tool that might work as a common planning platform for urban stakeholders interested in optimizing the ecological and public benefits associated with different urban greening strategies.

This project will help provide the underlying scientific basis to advance and refine the GAF concept in order to more effectively take account of human interactions with urban greening strategies, including environmental justice communities. Optimally, it could be applied to dense and diverse urban environments such as the mega-city of New York. As well as advancing scientific knowledge, the project will deliver a new planning tool that can provide a common platform for different New York City stakeholders to explore various greening strategies in the context of their different missions and goals. The project also will build a team of scientists, community organizations, and practitioners who can work together to create new knowledge on urban ecosystem sustainability and functionality. Research training will be provided to post-doctoral scholars, graduate students, and undergraduate students, and educational, outreach, and teaching activities will form major components of the project. This award was funded as an Urban Long-Term Research Area Exploratory (ULTRA-Ex) award as the result of a special competition jointly supported by the National Science Foundation and the U.S. Department of Agriculture Forest Service.

Decisions about "green electrical generation" are made both by energy consumers (who may choose to pay something extra) and by energy providers (who may choose to develop green power and to offer it to consumers). Social and environmental goals of both consumers and providers play an important role in this, as do social expectations about choices of green power by others. The interdisciplinary research program to be undertaken by this collaborative group, the Center for Research on Environmental Decisions, will focus on the social processes underlying group decisions (for example, the decision to develop or offer green options) as well as on processes underlying individual or household decisions (such as selection among different energy plans). Recent research on decision making has highlighted the importance of decision architecture -- the features of a decision setting that affect how preferences are constructed. Examples include whether outcomes are framed as gains or losses, what option is designated as default, and what temporal horizon is implied in the setting. The investigators will explore how decision architecture affects environmental decisions, especially those that are made in a social context and that usually involve uncertainty, long time horizon, and a mixture of goals, including social goals. The investigators will address social processes, decision architecture, and the use of technical information (including forecasts of climate variability and longer-term climate change) in environmental decision making by conducting laboratory experiments and field studies, the latter particularly focused on regions where there are useful year-to-year and decadal-scale forecasts of climate variation and significant impacts of this variation on livelihoods.

This collaborative group's work will enhance basic understanding about social processes and decision architecture. It also will advance decision design for complex climate-related decisions that involve long time scales, great uncertainty, and interdependence; and it will provide new insights regarding how technical information is used. It also will provide practical information and insights for multiple stakeholders at field sites, such as water managers, farming communities, insurance companies, and communities near retreating glaciers. The group's work also will provide new perspectives about the close collaboration and integration of social and natural science research. This collaborative group project is supported by the NSF Directorate for Social, Behavioral, and Economic Sciences through its Decision Making Under Uncertainty (DMUU) competition.

This collaborative group will continue ongoing efforts to study individual and group decision making under climate uncertainty and decision making in the face of environmental risk. The researchers will focus on synthesizing theoretical and empirical results of studies conducted over the last ten years in areas such as agriculture and water management. Three themes have connected the various research projects: the presentation and use of scientific information, the role of social context across different scales, and the effects of decision architecture. The collaborative group will synthesize project results generated across different sectors, cultures, and theoretical frameworks. The focus will be on the development of products that benefit the broad academic and practitioner communities, such as a book, educational videos, and tutorials. These materials will be available to inform future studies of environmental decision goals and processes and the communication of scientific information related to environmental science in order to motivate sustainability. The collaborative group will maintain its international network of expertise and researchers through virtual connections.

The investigators will synthesize theoretical and empirical results from their prior research generated across different sectors, cultures, and theoretical frameworks and translate those results into a form accessible to other researchers. Synthesis and integration efforts of sector-based field project results (in the areas of climate, water, hazards, energy) will enable researchers to infer the influence of central constructs from cognitive and social psychology in environmental perception and decisions. Comparing findings across sectors, particularly pertaining to the description-experience gap, will allow researchers to disentangle the effect of different elements of personal experience. Synthesis activities will focus on choice architecture, where behavioral decision theories and insights are turned into interventions. A meta-analysis of the relative effectiveness of different choice architecture interventions in different domains and with different populations of decision makers will generate a matrix, including data from this collaborative group and others. This collaborative group project is supported by the NSF Directorate for Social, Behavioral, and Economic Sciences through its Decision Making Under Uncertainty (DMUU) funding opportunity.