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The private sector is virtually nonexistent in Indian country. Consequently, reservations experience chronically high rates of unemployment and poverty. Tribes have implemented numerous laws to foster development; however, the private sector is yet to thrive. Legal uncertainty is a major reason why. Although tribes have the ability to make their own laws, the Supreme Court limits tribes’ ability to exercise jurisdiction over non-Indians. In 1981, the Supreme Court held tribes can exercise jurisdiction over non-Indians who enter a consensual relationship with the tribe or its citizens, and tribes can also assert jurisdiction over non-Indians engaged in behavior that imperils tribal welfare. These categories have been construed extremely narrowly. Furthermore, determining whether a transaction is subject to tribal jurisdiction often requires years of costly litigation. Another impediment to tribal economic development is state taxation because the Supreme Court permits states to tax Indian country commerce. This means tribes cannot collect taxes because this would result in dual taxation. Without tax revenue, tribes struggle to fund the infrastructure businesses need. Additionally, it is often unclear whether the state can regulate an activity in Indian country. As a result of these factors, businesses avoid Indian country.
The different types of uncertainty and the theories developed to account for them are presented in this chapter. The concepts of risk and reliability are introduced and defined following which basic probability ideas are discussed. The methods used to determine structural reliability are described (the direct integration method, Level II reliability methods and the Level I method). Level II reliability methods include the mean value first-order second-moment method and variants of it based on the Hasofer-Lind reliability index used in the case of nonlinear limit state functions and the Rackwitz-Fiessler procedure that has to be followed in the case of non-normal distributions. The Level I reliability method is discussed and the approach followed to determine partial safety factors described. In the next section fuzzy logic and fuzzy set theory are described. They are introduced by distinguishing between classical logic and fuzzy logic, following which fuzzy sets and fuzzy inference are described. In the last section the steps in the fuzzy inference system are presented and examples of such systems mentioned.
Legal risks are a significant part of a firm’s overall risk profile, and is typically guided by calculating the probability of risk and its potential magnitude. Yet this calcuation does not fully capture how risks manifest for organizations. This chapter presents a novel way of evaluating legal risk termed transformative legal risk management. Transformative legal risk management is different from traditional approaches because it incorporates a new layer of understanding legal risk. Using a four-pronged approach to risk management known as VUCA (volatility, uncertainty, complexity, and ambiguity). This chapter introduces two of the four VUCA risks: volatility and uncertainty. The chapter defines volatility, identifies sources of legal volatility, and presents responses that legal experts can use in response to volatility risk. The chapter then defines uncertainty, identifies sources of legal uncertainty, and presents responses firms can use to reduce uncertainty risk. The chapter shows how firms applying VUCA can not only minimize harm from legal risks but also elevate legal risk management into a practice that generates a competitive advantage over rivals.
Continuing the exploration of transformative legal risk management, this chapter addresses the remaining two risk dimensions that govern a VUCA environment: complexity and ambiguity. Complexity is an environment that contains numerous interconnected parts, accepts inputs, generates outputs, and develops a capacity to learn and remember. The fourth and final dimension of VUCA is ambiguity. Especially challenging for firms deploying legal knowledge, ambiguity is an environment where causes and effects propelling events forward are largely unknown, the firm does not know whether an organized system will emerge, and little historical precedent exists for determining the most appropriate course of action. The chapter defines complexity and ambiguity, explains how they are applicable to legal risk, and articulates strategies for firms to use their legal knowledge to anticipate and address complex and ambiguous legal problems.
According to the value-free ideal of science, scientists should draw their conclusions in a manner free of influence from value judgments. This ideal lends itself to a variety of interpretations and specifications. The ideal also faces numerous challenges that call into question not only whether it can be achieved but whether it really constitutes an ideal scientists ought to use to guide their actions. The chapter considers whether and in what conditions the value judgments of scientists might prevent or facilitate the achievement of scientific objectivity. From the role of value judgments in science the chapter turns to the closely related question of the appropriate role of scientists in the formulation of public policies. In many situations, the consideration of scientific evidence and scientific research bears importantly on questions of policy. The chapter then considers the complicated relationship between the reliance of policymakers on scientific expertise and the goals of democratic accountability and the public good.
There are two means of changing the expected value of a risk: changing the probability of a reward or changing the reward. Theoretically, the former produces a greater change in expected utility for risk averse agents. This paper uses two formats of a risk preference elicitation mechanism under two decision frames to test this hypothesis. After controlling for decision error, probability weighting, and order effects, subjects, on average, are slightly risk averse and prefer an increase in the expected value of a risk due to increasing the probability over a compensated increase in the reward. There is substantial across-format inconsistency but very little within-format inconsistency at the individual level.
Rounding is a common phenomenon when subjects provide an answer to an open-ended question, both in experimental tasks and in survey responses. From a statistical perspective, rounding implies that the measured variable is a coarsened version of the underlying continuous target variable. Since the coarsening process is non-random, inference from rounded data is generally biased. Despite the potentially severe consequences of rounding, little is known about its causes. In this paper, we focus on subjects’ uncertainty about the target variable as one potential cause for rounding behavior. We present a novel experimental method that induces uncertainty in a controlled way, thus providing causal evidence for the effect of subjects’ uncertainty on the extent of rounding. Then, we specify and estimate a mixture model that relates uncertainty and rounding. The results suggest that an increase in the exogenous level of uncertainty translates into higher variance of the subjects’ beliefs, which in turn results in more rounding.
Legal disputes are often negotiated under the backdrop of an adjudicated award. While settlements are common, they are not universal. In this paper, we empirically explore how uncertainty in adjudicated awards impacts settlement negotiations. To do so, we develop an experimental design to test how increases in variance and positive skewness of the award distribution impact negotiations and settlement rates. We find increases in variance decrease settlement rates, while increases in skewness generally increases settlement rates. We also gather individual measures of risk aversion and prudence, and incorporate these measures into the analysis to test for heterogeneous treatment effects. Overall, our results suggest that highly variable adjudicated awards can contribute to the excess use of inefficient litigation, while more positively skewed awards can reduce the use of inefficient litigation.
Most common pool resource (CPR) dilemmas share two features: they evolve over time and they are managed under environmental uncertainties. We propose a stylized dynamic model that integrates these two dimensions. A distinguishing feature of our model is that the duration of the game is determined endogenously by the users’ collective decisions. In the proposed model, if the resource stock level below which the irreversible event occurs is known in advance, then the optimal resource use coincides with a unique symmetric equilibrium that guarantees survival of the resource. As the uncertainty about the threshold level increases, resource use increases if users adopt decision strategies that quickly deplete the resource stock, but decreases if they adopt path strategies guaranteeing that the unknown threshold level is never exceeded. We show that under relatively high uncertainty about resource size, CPR users frequently implement decision strategies that terminate the game immediately. When this uncertainty is reduced, they maintain a positive resource level for longer durations.
We investigate the relative merits of the Boston and Serial Dictatorship mechanisms when the timing of students’ preference submission over schools varies within the structure of the mechanism. Despite the well-documented disadvantages of the Boston mechanism Abdulkadiroglu and Sonmez (American Economic Review 93:729–747 2003), we hypothesize that a Boston mechanism where students are required to submit their preferences before the realization of their exam scores, can in fact have fairness and efficiency advantages compared to the often favored Serial Dictatorship mechanism. We test these hypotheses in a series of laboratory experiments which vary by the class of mechanism implemented, and the preference submission timing by students, reflective of actual policy changes which have occurred in China. Our experimental findings confirm the efficiency hypothesis straightforwardly, and lend support to the fairness hypothesis when subjects have the chance to learn with experience. The results have important policy implications for school choice mechanism design when students’ relative rankings by schools are initially uncertain.
We study learning and selection and their implications for possible effort escalation in a simple game of dynamic property rights conflict: a multi-stage contest with random resolve. Accounting for the empirically well-documented heterogeneity of behavioral motives of players in such games turns the interaction into a dynamic game of incomplete information. In contrast to the standard benchmark with complete information, the perfect Bayesian equilibrium features social projection and type-dependent escalation of efforts caused by learning. A corresponding experimental setup provides evidence for type heterogeneity, for belief formation and updating, for self-selection and for escalation of efforts in later stages.
We conduct a framed field experiment in rural Ethiopia to test the seminal hypothesis that insurance provision induces farmers to take greater, yet profitable, risks. Farmers participated in a game protocol in which they were asked to make a simple decision: whether or not to purchase fertilizer and if so, how many bags. The return to fertilizer was dependent on a stochastic weather draw made in each round of the game. In later rounds a random selection of farmers made this decision in the presence of a stylized weather-index insurance contract. Insurance was found to have some positive effect on fertilizer purchases. Purchases were also found to depend on the realization of the weather in the previous round. We explore the mechanisms of this relationship and find that it may be the result of both changes in wealth weather brings about, and changes in perceptions of the costs and benefits to fertilizer purchases.
Despite public health efforts, uptake of preventive health technologies remains low in many settings. In this paper, we develop a formal model of prevention and test it through a laboratory experiment. In the model, rational agents decide whether to take up health technologies that reduce, but do not eliminate the risk of adverse health events. As long as agents are sufficiently risk averse and priors are diffuse, we show that initial uptake of effective technologies will be limited. Over time, the model predicts that take-up will decline as users with negative experiences revise their effectiveness priors towards zero. In our laboratory experiments, we find initial uptake rates between 65 and 80% for effective technologies with substantial declines over time, consistent with the model’s predictions. We also find evidence of decision-making not consistent with our model: subjects respond most strongly to the most recent health outcomes, and react to negative health outcomes by increasing their willingness to invest in prevention, even when health risks without prevention are known by all subjects. Our findings suggest that high uptake of preventive technologies should only be expected if the risk of adverse health outcomes without prevention is high, or if preventive technologies are so effective that the risk of adverse outcomes is negligible with prevention.
Quantum theory provides another way to formalize uncertainty. Quantum probability theory can be used to model phenomena such as order effects which cannot be straightforwardly modeled within classical probability theory. Key concepts of quantum theory including superposition states, noncommutative operations, and entanglement provide new angles and explanations for some predictive phenomena.
Epidemic preparedness requires clear procedures and guidelines when a rapid risk assessment of a communicable disease threat is requested. In an evaluation of past risk assessments, we found that modifications to existing guidelines, such as the European Centre for Disease Prevention and Control’s (ECDC) rapid risk assessment operational tool, can strengthen this process. Therefore, we present alternative guidelines, in which we propose a unifying risk assessment terminology, describe how the risk question should be phrased by the risk manager, and redefine the probability and impact dimension of risk, including a methodology to express uncertainty. In our approach, probability refers to the probability of the introduction of a disease into a specified population in a specified time period, and impact combines the magnitude of spread and the severity of the health outcomes. Based on the collected evidence, both the probability of introduction and the magnitude of spread are quantitatively expressed by expert judgements, providing unambiguous risk assessment. We advise not to summarize the risk by a single qualification as ‘low’ or ‘high’. These alternative guidelines, which are illustrated by a hypothetical example on mpox, have been implemented at Statens Serum Institut in Denmark and can benefit other public health institutes.
Why do US voters allow politicians to hold the country’s economy hostage during debt ceiling negotiations? In this research note, we argue that ignorance and uncertainty over the consequences of a debt ceiling breach play a nontrivial role in public support for hard-line negotiating positions. In a pre-registered survey experiment, two weeks before the June 2023 deadline to raise the US debt ceiling, we show that providing credible information about the consequences of default increases support for concessions among both Democrats and Republicans. Further, more certain information about the consequences of a debt ceiling breach has a larger effect than less-certain information suggesting that the unpredictable consequences of the crisis also help explain voter reluctance to accept concessions. The findings have implications for understanding debt ceiling negotiations and other crisis bargaining situations where the public serves as a relevant third party.
This essay is in celebration of the contributions of Mario Rizzo. I argue that among contemporary economists in the Austrian School of Economics tradition it is Rizzo that advanced, more than his contemporaries, the scientific research program of rational choice as if the choosers were human beings; the dynamic subjectivism and the agony of choice and social interaction; the causal processes and the institutional dynamics that make up a complex social order; and the role of law, politics and civil society in shaping commercial life. In making this argument, I attempt to arbitrage the contributions of two essays of Rizzo’s: ‘Law Amid Flux’ and ‘The Genetic-Causal Tradition and Modern Economic Theory’.
We introduce stochastic loss into a gift exchange game to study how information on intentions affects reciprocity. In one treatment, the respondent observes the amount received and whether a loss occurred, so both the consequential outcome and the sender’s original intention are known. In the other two treatments, information about whether a loss occurred is hidden, and the respondent is only informed of the amount received (outcome) or the amount initially sent (intention). Using both regression-based approaches and non-parametric tests, we find greater reciprocity in the two treatments that reveal intentions. These differences arise even in a simple one-shot setting without reputational benefits and are economically meaningful; they are similar in magnitude to the difference attributable to a full point reduction in the amount received. Our findings show the impact of the information environment on reciprocity in settings with uncertainty and suggest that transparency is important to reciprocity.
When affirmative action policies target more than one disadvantaged group, they contain uncertainty as to whether an individual who belongs to one of these groups was actually favored. In a laboratory experiment, we study how this feature affects outcomes of affirmative action in the form of quotas, and compare it with two other conditions, namely affirmative action with a certain favored group and no affirmative action. We find that when a group is favored with certainty and the social identity that triggers affirmative action is made salient, affirmed individuals are wrongly perceived as less competent, both by themselves and by others. Consequently, their willingness to compete does not increase and they are selected less for teamwork post competition. Affirmative action with uncertain favored groups does not distort belief in competence, and thus does not induce such unintended consequences. In contrast, it increases competition entry of the affirmed groups and enhances their chances of being selected for teamwork.
This chapter first describes the generic situations of risk, subjective uncertainty, ambiguity, and true uncertainty. It then outlines the main decision theory under risk in neoclassical economics, expected utility theory (EU). We outline EU’s axiomatic structure and highlight the independence axiom that has often been rejected by the evidence, as exemplified by the Allais paradox. The two main drawbacks of EU are that it does not allow for individuals who (i) derive utility from changes in outcomes relative to a reference point (although this is consistent with it's underlying axioms) and (i) weight probabilities in a nonlinear manner. Other violations of EU are also considered; these include description invariance and preference reversals.