Camilla Francesca Colombo

There are public projects which many people welcome because they are expected to be beneficial for society at large. On the other hand, however, these projects may generate larger negative externalities for certain parts of society. One example is the erection of a nuclear power-plant, a measure that is widely considered to render a country’s energy provision less dependent on supply from outside. On the other hand, it possibly causes a feeling of insecurity among people who live in the vicinity of such a power station, and can furthermore have negative economic consequences in so far as the price of land and of houses may decline in this area. How should decisions on public projects with larger negative externalities be taken? Assuming that the benefits for all those who are directly or indirectly affected by such a project are roughly the same, a cost-sharing or burden-sharing method is proposed that focuses on a weighted difference between the economic performance of a particular region and the average performance of all regions concerned. The weight is to be determined by an impartial observer.

This paper examines Kahneman and Tversky’s standard explanation for preference reversal due to framing effects in the famous “Asian flu” case. It argues that, alongside with their “loss/no gain effect” account, an alternative interpretation, still consistent with the empirical data, amounts to a more reasonable psychological explanation for the preference reversal. Specifically, my hypothesis is that shifts in the baseline induce shifts in the agents’ classification of the same action as “doing harm” rather than “allowing harm to occur”, and that people are risk-seeking when it comes to avoid causing extra deaths–doing harm–and risk-averse when it comes to preventing more deaths–by the means of allowing other deaths to occur as a side effect. I then survey the two most influential concurrent accounts in the moral literature, with respect to the relation between the loss/no gain and the doing/allowing distinction: Horowitz’s reductionist conclusion, which argues that the latter collapses into the former, and Kamm’s rebuttal, which claims instead that the two distinctions can be pulled apart. I eventually explain why my interpretation differs from both these positions.

There are public projects which many people welcome because they are expected to be beneficial for society at large. On the other hand, however, these projects may generate larger negative externalities for certain parts of society. One example is the erection of a nuclear power-plant, a measure that is widely considered to render a country’s energy provision less dependent on supply from outside. On the other hand, it possibly causes a feeling of insecurity among people who live in the vicinity of such a power station, and can furthermore have negative economic consequences in so far as the price of land and of houses may decline in this area. How should decisions on public projects with larger negative externalities be taken? Assuming that the benefits for all those who are directly or indirectly affected by such a project are roughly the same, a cost-sharing or burden-sharing method is proposed that focuses on a weighted difference between the economic performance of a particular region and the average performance of all regions concerned. The weight is to be determined by an impartial observer.

Intuitively, an agent who does harm behaves differently from an agent who allows harm to happen. This thesis examines the distinction between doing harm and merely allowing it to occur. I argue that this distinction is morally relevant, and doing harm is harder to justify than allowing harm, but that there is not always a fact of the matter how the distinction ought to be drawn. In Chapters 1 and 2, I survey the main alternative accounts for explaining the difference between “doing” and “allowing”. I compare causal approaches, which distinguish doing and allowing on the basis of how an agent caused an outcome, with “norm-based” accounts, which explain the distinction appealing to independent moral features. I conclude that a “mixed” causal account, such as Hitchcock's self-contained network model, is the most promising for tracking doing/allowing classifications. I then examine whether this distinction is morally relevant. I outline two theoretical hypotheses, the “positive” and the “negative” theses. The former argues that there is a fact of the matter whether an action is “doing” or “allowing”, and this classification is morally significant; the latter that there might or might not be such a fact of the matter, but in any case this distinction is not morally relevant. In Chapter 3, I critique an influential strategy for settling this issue, that is, comparing “fully-equalized cases”. In Chapter 4, I consider the import of “framing effects”. Despite attempts to use fully-equalized cases or evidence of framing in support of either thesis, these strategies are not compelling. In Chapters 5 and 6, I present my alternative thesis, which relies on the self-contained network model. I define “doings” as instances where an outcome counterfactually depends on the agent, within a “self-contained” network, and “allowings” as instances where the outcome depends on the agent, within a “non-self-contained” network. This classification captures whether an agent is causally relevant to an outcome in a specific way; nonetheless, the identification of “selfcontained” networks incorporates agents' empirical and normative expectations. The distinction is thus morally relevant, as it captures moral considerations, but may be ultimately ambiguous, as there may not always be a fact of the matter as to how the distinction should be drawn.

Intuitively, an agent who does harm behaves differently from an agent who allows harm to happen. This thesis examines the distinction between doing harm and merely allowing it to occur. I argue that this distinction is morally relevant, and doing harm is harder to justify than allowing harm, but that there is not always a fact of the matter how the distinction ought to be drawn. In Chapters 1 and 2, I survey the main alternative accounts for explaining the difference between “doing” and “allowing”. I compare causal approaches, which distinguish doing and allowing on the basis of how an agent caused an outcome, with “norm-based” accounts, which explain the distinction appealing to independent moral features. I conclude that a “mixed” causal account, such as Hitchcock's self-contained network model, is the most promising for tracking doing/allowing classifications. I then examine whether this distinction is morally relevant. I outline two theoretical hypotheses, the “positive” and the “negative” theses. The former argues that there is a fact of the matter whether an action is “doing” or “allowing”, and this classification is morally significant; the latter that there might or might not be such a fact of the matter, but in any case this distinction is not morally relevant. In Chapter 3, I critique an influential strategy for settling this issue, that is, comparing “fully-equalized cases”. In Chapter 4, I consider the import of “framing effects”. Despite attempts to use fully-equalized cases or evidence of framing in support of either thesis, these strategies are not compelling. In Chapters 5 and 6, I present my alternative thesis, which relies on the self-contained network model. I define “doings” as instances where an outcome counterfactually depends on the agent, within a “self-contained” network, and “allowings” as instances where the outcome depends on the agent, within a “non-self-contained” network. This classification captures whether an agent is causally relevant to an outcome in a specific way; nonetheless, the identification of “selfcontained” networks incorporates agents' empirical and normative expectations. The distinction is thus morally relevant, as it (also) captures moral considerations, but may be ultimately ambiguous, as there may not always be a fact of the matter as to how the distinction should be drawn.

Can rational agents coordinate in simultaneous interactions? According to standard game theory they cannot, even if there is a uniquely best way of doing so. To solve this problem we propose an argument in favor of ‘belief-less reasoning’, a mode of inference that leads to converge on the optimal solution ignoring the beliefs of the other players. We argue that belief-less reasoning is supported by a commonsensical Principle of Relevant Information that every theory of rational decision must satisfy. We show that this principle can be used to justify (some versions of) team reasoning, as well as other schemes of practical reasoning that do not involve sophisticated meta-representation.