123,337 research outputs found

    Evidence and rationalization

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    Suppose that you have to take a test tomorrow but you do not want to study. Unfortunately you should study, since you care about passing and you expect to pass only if you study. Is there anything you can do to make it the case that you should not study? Is there any way for you to ‘rationalize’ slacking off? I suggest that such rationalization is impossible. Then I show that if evidential decision theory is true, rationalization is not only possible but sometimes advisable

    Reason-Based Rationalization

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    [This version of the paper has been superseded by "Reason-based choice and context-dependence: An explanatory framework", forthcoming in Economics & Philosophy.] We introduce a “reason-based” way of rationalizing an agent’s choice behaviour, which explains choices by specifying which properties of the options or choice context the agent cares about (the “motivationally salient properties”) and how he or she cares about these properties (the “fundamental preference relation”). Reason-based rationalizations can explain non-classical choice behaviour, including boundedly rational and sophisticated rational behaviour, and predict choices in unobserved contexts, an issue neglected in standard choice theory. We characterize the behavioural implications of different reason-based models and distinguish two kinds of context-dependent motivation: “context-variant” motivation, where the agent cares about different properties in different contexts, and “context-regarding” motivation, where the agent cares not only about properties of the options, but also about properties relating to the context

    Stranded Capital in Fisheries: The Pacific Coast Groundfish/Whiting Case

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    Current rationalization options for West Coast groundfish trawl fisheries include significant allocations of harvester quota to processors, justified as compensation for “stranded capital.†This article discusses the origin of the concept of stranded capital, its use in other policy settings, preconditions, measurement, and remedies for addressing it. Our main finding is that rationalization of fisheries is unlikely to generate significant processing stranded capital. Most capital involved in fisheries processing is malleable and not likely to be devalued as a result of rationalization. If policy makers nevertheless judge it desirable to consider compensation, a legitimate process would tie compensation to anticipated or demonstrated capital losses. Current policies proposed on the U.S. West Coast to transfer harvester quota are arbitrary and unsupported by empirical estimates of the magnitude of the problem. They are likely to generate important spillover effects that could negate some of the intended benefits of rationalization.Stranded capital, rationalization, malleable capital, processor compensation, Industrial Organization, Political Economy, Q22,

    Implications of Pareto Efficiency for Two-Agent (Household) Choice

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    We study when two-member household choice behavior is compatible with Pareto optimality. We ask when an external observer of household choices, who does not know the individuals' preferences, can rationalize the choices as being Pareto-optimal. Our main contribution is to reduce the problem of rationalization to a graph-coloring problem. As a result, we obtain simple tests for Pareto optimal choice behavior. In addition to the tests, and using our graph-theoretic representation, we show that Pareto rationalization is equivalent to a system of quadratic equations being solvable

    Catch Shares in Action: United States Bering Sea and Aleutian Islands Crab Rationalization Program

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    The Bering Sea and Aleutian Islands (BSAI) Crab Rationalization Program (the Rationalization Program) was designed to improve resource conservation, operating efficiency and fishermen's safety while maintaining participation by remote communities. A number of important features account for the diverse natures of stakeholders and the fishery's historical importance to many communities. These include: a unique three-pie approach that defines and assigns different types of privileges to vessel owners, crew and processors; an industry-funded, government-operated loan program to assist new entrants and crew; and voluntary Cooperatives that assist in program administration and fishing coordination

    Causal Modeling and the Efficacy of Action

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    This paper brings together Thompson's naive action explanation with interventionist modeling of causal structure to show how they work together to produce causal models that go beyond current modeling capabilities, when applied to specifically selected systems. By deploying well-justified assumptions about rationalization, we can strengthen existing causal modeling techniques' inferential power in cases where we take ourselves to be modeling causal systems that also involve actions. The internal connection between means and end exhibited in naive action explanation has a modal strength like that of distinctively mathematical explanation, rather than that of causal explanation. Because it is stronger than causation, it can be treated as if it were merely causal in a causal model without thereby overextending the justification it can provide for inferences. This chapter introduces and demonstrate the usage of the Rationalization condition in causal modeling, where it is apt for the system(s) being modeled, and to provide the basics for incorporating R variables into systems of variables and R arrows into DAGs. Use of the Rationalization condition supplements causal analysis with action analysis where it is apt

    The Complexity of Rationalizing Network Formation

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    We study the complexity of rationalizing network formation. In this problem we fix an underlying model describing how selfish parties (the vertices) produce a graph by making individual decisions to form or not form incident edges. The model is equipped with a notion of stability (or equilibrium), and we observe a set of "snapshots" of graphs that are assumed to be stable. From this we would like to infer some unobserved data about the system: edge prices, or how much each vertex values short paths to each other vertex. We study two rationalization problems arising from the network formation model of Jackson and Wolinsky [14]. When the goal is to infer edge prices, we observe that the rationalization problem is easy. The problem remains easy even when rationalizing prices do not exist and we instead wish to find prices that maximize the stability of the system. In contrast, when the edge prices are given and the goal is instead to infer valuations of each vertex by each other vertex, we prove that the rationalization problem becomes NP-hard. Our proof exposes a close connection between rationalization problems and the Inequality-SAT (I-SAT) problem. Finally and most significantly, we prove that an approximation version of this NP-complete rationalization problem is NP-hard to approximate to within better than a 1/2 ratio. This shows that the trivial algorithm of setting everyone's valuations to infinity (which rationalizes all the edges present in the input graphs) or to zero (which rationalizes all the non-edges present in the input graphs) is the best possible assuming P ≠ NP To do this we prove a tight (1/2 + δ) -approximation hardness for a variant of I-SAT in which all coefficients are non-negative. This in turn follows from a tight hardness result for MAX-LlN_(R_+) (linear equations over the reals, with non-negative coefficients), which we prove by a (non-trivial) modification of the recent result of Guruswami and Raghavendra [10] which achieved tight hardness for this problem without the non-negativity constraint. Our technical contributions regarding the hardness of I-SAT and MAX-LIN_(R_+) may be of independent interest, given the generality of these problem

    Crime and Punishment Again: The Economic Approach with a Psychological Twist

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    Akerlof and Dickens (1982) suggested that in a model of criminal behavior which considered the effects of cognitive dissonance, increasing the severity of punishment could increase the crime rate. This paper demonstrates that that conjecture was correct. With cognitive dissonance, people may have to rationalize not committing crimes under normal circumstances if punishment is not severe. The rationalization may lead them to underestimate the expected utility of committing crimes when opportunities present themselves. If punishment is severe, then rationalization may not be necessary and people may be more likely to commit crimes when opportunities arise.
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