Aron Vallinder

In this chapter, I argue that the field of cultural evolution can usefully inform attempts to understand and influence the long-term future. First, I provide an overview of cultural evolution, covering what it means for culture to evolve, the mechanisms by which it happens, the crucial importance of cumulative cultural evolution for hunan history, and how cultural evolution (and in particular intergroup competition) has driven the rise of large-scale cooperation. Second, I draw out some possible lessons from cultural evolution for thinking about the long-term future. In particular, I suggest we should be careful not to prematurely ”lock in” certain values or other cultural features, and instead aim for a society open to variation and competition. I also consider whether the future will bring greater selection pressure for particular kinds of values, such as patience.

Suppose that an altruistic agent who is uncertain between evidential and causal decision theory finds herself in a situation where these 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 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 are not very different, then it is appropriate to choose the option that 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.

According to the traditional Bayesian view of credence, its structure is that of precise probability, its objects are descriptive propositions about the empirical world, and its dynamics are given by conditionalization. Each of the three essays that make up this thesis deals with a different variation on this traditional picture. The first variation replaces precise probability with sets of probabilities. The resulting imprecise Bayesianism is sometimes motivated on the grounds that our beliefs should not be more precise than the evidence calls for. One known problem for this evidentially motivated imprecise view is that in certain cases, our imprecise credence in a particular proposition will remain the same no matter how much evidence we receive. In the first essay I argue that the problem is much more general than has been appreciated so far, and that it’s difficult to avoid without compromising the initial evidentialist motivation. The second variation replaces descriptive claims with moral claims as the objects of credence. I consider three standard arguments for probabilism with respect to descriptive uncertainty—representation theorem arguments, Dutch book arguments, and accuracy arguments—in order to examine whether such arguments can also be used to establish probabilism with respect to moral uncertainty. In the second essay, I argue that by and large they can, with some caveats. First, I don’t examine whether these arguments can be given sound non-cognitivist readings, and any conclusions therefore only hold conditional on cognitivism. Second, decision-theoretic representation theorems are found to be less convincing in the moral case, because there they implausibly commit us to thinking that intertheoretic comparisons of value are always possible. Third and finally, certain considerations may lead one to think that imprecise probabilism provides a more plausible model of moral epistemology. The third variation considers whether, in addition to conditionalization, agents may also change their minds by becoming aware of propositions they had not previously entertained, and therefore not previously assigned any probability. More specifically, I argue that if we wish to make room for reflective equilibrium in a probabilistic moral epistemology, we must allow for awareness growth. In the third essay, I sketch the outline of such a Bayesian account of reflective equilibrium. Given that this account gives a central place to awareness growth, and that the rationality constraints on belief change by awareness growth are much weaker than those on belief change by conditionalization, it follows that the rationality constraints on the credences of agents who are seeking reflective equilibrium are correspondingly weaker.

Epistemologists can be divided into two camps: those who think that nothing short of certainty or (subjective) probability 1 can warrant assertion and those who disagree with this claim. This paper addressed this issue by inquiring into the problem of setting the probability threshold required for assertion in such a way that that the social epistemic good is maximized, where the latter is taken to be the veritistic value in the sense of Goldman (Knowledge in a social world, 1999). We provide a Bayesian model of a test case involving a community of inquirers in a social network engaged in group deliberation regarding the truth or falsity of a proposition $p.$ p . Results obtained by means of computer simulation indicate that the certainty rule is optimal in the limit of inquiry and communication but that a lower threshold is preferable in less idealized cases

According to the Argument from Disagreement (AD) widespread and persistent disagreement on ethical issues indicates that our moral opinions are not influenced by moral facts, either because there are no such facts or because there are such facts but they fail to influence our moral opinions. In an innovative paper, Gustafsson and Peterson (Synthese, published online 16 October, 2010) study the argument by means of computer simulation of opinion dynamics, relying on the well-known model of Hegselmann and Krause (J Artif Soc Soc Simul 5(3):1–33, 2002; J Artif Soc Soc Simul 9(3):1–28, 2006). Their simulations indicate that if our moral opinions were influenced at least slightly by moral facts, we would quickly have reached consensus, even if our moral opinions were also affected by additional factors such as false authorities, external political shifts and random processes. Gustafsson and Peterson conclude that since no such consensus has been reached in real life, the simulation gives us increased reason to take seriously the AD. Our main claim in this paper is that these results are not as robust as Gustafsson and Peterson seem to think they are. If we run similar simulations in the alternative Laputa simulation environment developed by Angere and Olsson (Angere, Synthese, forthcoming and Olsson, Episteme 8(2):127–143, 2011) considerably less support for the AD is forthcoming

The paper presents and defends a Bayesian theory of trust in social networks. In the first part of the paper, we provide justifications for the basic assumptions behind the model, and we give reasons for thinking that the model has plausible consequences for certain kinds of communication. In the second part of the paper we investigate the phenomenon of overconfidence. Many psychological studies have found that people think they are more reliable than they actually are. Using a simulation environment that has been developed in order to make our model computationally tractable we show that in our model inquirers are indeed sometimes better off from an epistemic perspective overestimating the reliability of their own inquiries. We also show, by contrast, that people are rarely better off overestimating the reliability of others. On the basis of these observations we formulate a novel hypothesis about the value of overconfidence

Traditional Bayesianism requires that an agent’s degrees of belief be represented by a real-valued, probabilistic credence function. However, in many cases it seems that our evidence is not rich enough to warrant such precision. In light of this, some have proposed that we instead represent an agent’s degrees of belief as a set of credence functions. This way, we can respect the evidence by requiring that the set, often called the agent’s credal state, includes all credence functions that are in some sense compatible with the evidence. One known problem for this evidentially motivated imprecise view is that in certain cases, our imprecise credence in a particular proposition will remain the same no matter how much evidence we receive. In this article I argue that the problem is much more general than has been appreciated so far, and that it’s difficult to avoid it without compromising the initial evidentialist motivation. _1_ Introduction _2_ Precision and Its Problems _3_ Imprecise Bayesianism and Respecting Ambiguous Evidence _4_ Local Belief Inertia _5_ From Local to Global Belief Inertia _6_ Responding to Global Belief Inertia _7_ Conclusion