Choice Group

In Rational Powers in Action (Oxford, forthcoming), I defend what might be called an "action-first" conception of instrumental rationality; that is, a conception of instrumental rationality that takes intentional action as the fundamental category of the theory of instrumental rationality. In this talk, I will first explain more precisely the commitments of an action-first model as
well as the central tenets of the specific version of the model I defend. I then outline the main attractions and advantages of such a view, especially with regards of how it can deal with the extended nature of agency. However, the model seems to face a major obstacle. The central principles of the model seem to have no application for risky contexts: its two central principles (a means-end principle of derivation and a coherence principle that requires the agent not to pursue incompatible ends) presuppose a context of knowledge for their proper application. I try to show, however, that there are promising ways to extend this model to contexts of risk and uncertainty, and that the model can both incorporate the insights of decision theory in some of these contexts, as well as providing plausible accounts of ordinary choice dispositions that are puzzling from the point of view of orthodox decision theory.

Two of our PhD students, Ze’ev Goldschmidt & Nick Makins, present their research to the Choice Group.

Do people tend to be overconfident in their opinions? Many psychologists think so. They have run calibration studies in which they ask people a variety of questions, and then test whether their confidence in their answers matches the proportions of those answers that are true. Under certain conditions, an “overconfidence effect” is robust—for example, of the answers people are 80% confident in, only 60% are true. Psychologists have inferred that people tend to be irrationally overconfident. My question is when and why this inference is warranted. Although it may at first appear hopelessly flawed, I show that under controlled conditions it is a warranted inference. However, I argue that real-world studies standardly fail to meet these conditions—and, as a result, that rational people can often be expected to display the “overconfidence effect.” Thus in order to test whether people are overconfident, we must first predict whether and to what extent a rational person will display this effect, and then compare that prediction to real people’s performance. I show how in principle this can be done—and that doing so may overturn the standard interpretation of robust empirical effects. Upshot: have much less evidence for overconfidence than many have thought.

In this talk, I discuss the role of consent in permissible sexual interaction. I’ll suggest that the focus on consent as an individual act may be a symptom of a narrow focus on individual rights and permissions as framing the moral domain; but that joint, co-operative activity is not well fitted into this normative vocabulary. This raises a further puzzle. It is a manifest sociological fact that consent, and competence at tracking its presence or absence, has come to the forefront of the attempted regulation of the activities of teenagers and young adults. What are we to make of this focus, if it is not grounded in our understanding of the wrongs of bad sex? I offer a speculative hypothesis about this sociology, and draw out a couple of normative consequences.

On the Lockean thesis one ought to believe a proposition if and only if one assigns it a credence at or above a threshold (Foley 1992). The Lockean thesis, thus, provides a way of linking sets of all-or-nothing beliefs with credences. Recent work on the lexical semantics of attitude verbs such a 'think’ and ‘believe’ suggest that Lockeanism is more plausible than the view that believing a proposition requires having full confidence in it (Hawthorne, Rothschild and Spectre, 2016). In this talk, I will give two independent characterizations of sets of full beliefs satisfying the Lockean thesis. One is in terms of betting dispositions associated with full beliefs and one is in terms of an accuracy scoring system for full beliefs. These characterizations are parallel to, but not merely derivative from, the more familiar Dutch book (de Finetti 1974) and accuracy arguments (Joyce 1998) for probabilism.

Due to the current COVID-19 situation this event will now take place online via Zoom.  Everyone is welcome to join this event using a computer with access to the internet and Zoom. To take part just follow these instructions: Download Zoom Join the event anytime after 4pm via this link: https://lse.zoom.us/j/948376433 At 4:30pm the event begins, with Q&A at 5:30pm. The event will…

Unfortunately, due to the current COVID-19 situation this event will no longer take place as planned. We apologise for any inconvenience caused.

Many pressing social problems are the result of undesirable human behavior. People pollute too much, fail to do enough to help the disadvantaged, and ignore government advice during pandemics. The dominant approach to predicting the effectiveness of interventions aimed at changing such behavior is the evidence-based approach. As Cartwright and Hardie describe it, this approach asks the predictor to construct an argument for the effectiveness of an intervention and then to support each premise of the effectiveness argument with evidence – facts about the world.

Unfortunately, the evidence-based approach is highly unreliable in certain cases (e.g., when the proposed intervention is novel, ambitious, complex, and not conducive to small-scale experimentation). Yet we need not resign ourselves to unreliable effectiveness predictions in such cases. We can use intuition grounded in folk psychology (our informal knowledge of others’ behavior) to predict the effectiveness of behavioral policy interventions.

However, relying on untutored intuition alone is problematic. I advocate instead an approach that relies on both intuition and evidence (while also being systematic and explicit about the non-intuitive inputs into the prediction). I argue that this sophisticated intuitive approach to predicting the effectiveness of behavioral policy interventions is more reliable, at least in certain cases, than the evidence-based approach.

The mathematical foundations of imprecise probability theory (IP) have been in place for 25 years, and IP has proved successful in practice. But IP methods lack rigorous accuracy-centered, philosophical justifications. Traditional Bayesian methods can be justified using epistemic scoring rules, which measure the accuracy of the estimates that they produce. But there has been little work extending these justifications to the IP framework. In this talk, I will first outline some initial work developing scoring rules for imprecise probabilities: IP scoring rules. Then I will explain why a range of impossibility results for IP scoring rules should not concern us. Finally, I will use IP scoring rules to engineer a new method for aggregating imprecise forecasts.

Abstract: TBA

Two of our PhD students present their research to the Choice Group

Suppose that an altruistic and morally motivated agent who is uncertain between evidential decision theory (EDT) and causal decision theory (CDT) finds herself in a situation in which the two theories give conflicting verdicts. We argue that even if she has significantly higher credence in CDT, she should nevertheless act in accordance with EDT. First, we claim that that the appropriate response to normative uncertainty is to hedge one’s bets. That is, if the stakes are much higher on one theory than another, and the credences you assign to each of these theories aren’t very different, then it’s appropriate to choose the option which performs best on the high-stakes theory. Second, we show that, given the assumption of altruism, the existence of correlated decision-makers will increase the stakes for EDT but leave the stakes for CDT unaffected. Together these two claims imply that whenever there are sufficiently many correlated agents, the appropriate response is to act in accordance with EDT.

Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Most of this work focuses on strategies for calculating the mean credence function of the group. In particular, Moss (2011) and Pettigrew (2019) argue that group credences should be calculated by taking a linear mean of the credences of each individual in the group, on the grounds that this method leads to more accurate group credences than all other methods. In this paper, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should not generate group credences by finding the mean of the credences of the individuals in a group. Rather, where possible, we should aggregate the underlying statistical models that individuals use to generate their credence function, using "stacking" techniques from statistics and machine learning first developed by Wolpert (1992). My argument draws on a result by Le and Clarke (2017) that shows the power of stacking techniques to generate predictively accurate aggregations of statistical models, even when all models being aggregated are highly inaccurate.

In a series of papers over the past twenty years, and in a new book, Igor Douven has argued that Bayesians are too quick to reject versions of inference to the best explanation or abduction that cannot be accommodated within their framework. In this paper, I survey Douven’s worries and bring to bear a series of pragmatic and purely epistemic arguments to show that Bayes’ Rule really is the only correct way to respond to your evidence.

In his two famous papers of 1953 and 1955 defending Utilitarianism, Harsanyi draws on the same simple idea: that to determine what is morally best we should put ourselves in the shoes of an impartial, but sympathetic, rational evaluator of states of affairs that differ in terms of the wellbeing of the various individuals within them. In this talk, I will pursue a similar thought experiment, but depart from Harsanyi in two ways. Firstly, I will allow that the impartial or social evaluator take attitudes to uncertainty that are ruled out by expected utility theory, but which various rival theories of rationality regard as reasonable; especially in situations of ambiguity in which the evaluator has insufficient information to assign precise probabilities to the various possible states of affairs. Secondly, I will allow that they take attitudes to inequality that are disallowed by Utilitarianism but which are regarded as reasonable (or even mandatory) by rival social ethics. Despite this permissiveness, it turns out that impartial evaluation governed by minimal conditions of rationality and sympathy impose strong constraints on evaluations of individual and social wellbeing.

For those of us interested in developing norms for effective scientific advising, the SAGE minutes (59 sets of which are now publicly available) are a valuable resource. Drawing on these minutes, I consider the wider lessons for norms of scientific advising that can be learned from the UK's initial response to coronavirus, highlighting five key issues: (i) the division of advisory labour, (ii) the grain of recommendations, (iii) the role of worst-case scenarios, (iv) switching cost asymmetries and (v) the difference between independence and neutrality.