Expected Value Under Normative Uncertainty September 20191 Franz Dietrich Paris School of Economics & CNRS Brian Jabarian Paris School of Economics & Princeton University Abstract Maximising expected value is the classic doctrine in choice theory under empirical uncertainty, and a prominent proposal in the emerging philosophical literature on normative uncertainty, i.e., uncertainty about the standard of evaluation. But how should Expectationalism be stated in general, when we can face both uncertainties simultaneously, as is common in life? Surprisingly, different possibilities arise, ranging from Ex-Ante to Ex-Post Expectationalism, with several hybrid versions. The difference lies in the perspective from which expectations are taken, or equivalently the amount of uncertainty packed into the prospect evaluated. Expectationalism thus faces the classic dilemma between ex-ante and ex-post approaches, familiar elsewhere in ethics and aggregation theory under uncertainty. We analyse the spectrum of expectational theories, showing that they reach diverging evaluations, use different modes of reasoning, take different attitudes to normative risk as well as empirical risk, but converge under an interesting (necessary and suffi cient) condition. 1 The problem When evaluating choice options, we often face two types of uncertainty. Decision theory has focused on empirical uncertainty: uncertainty about empirical facts, e.g., facts about weather or election outcomes. Philosophers have recently turned to normative uncertainty: uncertainty about the standard of evaluation, due for instance to competing normative intuitions. Important contributions include Oddie (1994), Lockhart (2000), Jackson and Smith (2006), MacAskill (2014, 2016), Bradley and Drechsler (2014), Sepielli (2009), Weatherson (2014), Lazar (2017), Greaves and Cotton-Barratt (2019), Tarsney (2018a, 2019), MacAskill and Ord (forth.), Riedener (2020), and Dietrich and Jabarian (2019a). Normative uncertainty is omnipresent in deliberation and decision-making: parents may wonder how much they should value child autonomy, 1Acknowledgements to be added. 1 even when certain about all relevant empirical facts; agents may wonder which consequences matter most, and whether deontological considerations matter too; reasonbased agents (as in Dietrich and List 2013, 2017) may wonder which properties matter, and how they matter; and so on. Much can be debated about the metaphysical status of normative uncertainty: Is it uncertainty about subjective or objective facts? About real or constructed facts? And so forth. We put these important debates aside. But we stress two things: normative uncertainty is meaningful under many interpretations and metaethical views, and it is not formally reducible to standard choice-theoretic uncertainty.2 A prominent proposal in the theory of normative uncertainty is to maximise expected value across standards of evaluation, an approach pioneered in some of the cited works, by Oddie, MacAskill, and Ord, respectively. Riedener (2020) gives a sophisticated axiomatic characterization. Much of today's debate focuses on whether this approach -to be called Expectationalism -is justified. Expectationalism has for instance been defended through analogies with empirical uncertainty and through formal arguments (e.g., Riedener's work), but criticized for relying on a precise quantification of normative uncertainty and on certain measurements and comparisons of value (e.g., Tarsney 2018b). Instead of addressing the important question 'Expectationalism or not?', we shall assume Expectationalism, and ask a new question: 'Expected value of what?' A particular answer was so far taken for granted: expected value of the option itself. We call this Standard Expectationalism. Standard Expectationalism is hybrid in nature: it reasons 'empirically ex-ante', but 'normatively ex-post', as will be shown. There are two non-hybrid incarnations of Expectationalism, Ex-Ante Expectationalism and Ex-Post Expectationalism, which form the expected value of prospects that respectively contain all uncertainty (empirical or normative) or no uncertainty. Between these two extremes, there is a spectrum of hybrid (semi-ex-ante) versions of Expectationalism. The dilemma between ex-ante and ex-post reasoning is prominent in other fields of formal ethics and aggregation theory; see Diamond (1976), McCarthy (2006, 2008, 2015), Fleurbaey (2010), Fleurbaey and Voorhoeve (2016), and Fleurbaey and Zuber (2017). The theory of normative uncertainty cannot escape this dilemma. Just as social egalitarians face a dilemma between ex-ante and ex-post equality, so expectationalists about normative uncertainty face a dilemma between ex-ante, ex-post, and hybrid formulations of Expectationalism. The dilemma always takes the same form: should a given paradigm or ideal - for instance equality or (in our case) expected value - be pursued from an ex-ante or ex-post perspective? The ex-ante/ex-post dilemma reaches also into non-expectational approaches to normative uncertainty, such as maxmin approaches, of which one could again envision ex-ante, ex-post, and hybrid versions. 2Some decision-theoretic models can be reinterpreted in terms of normative uncertainty. Examples are multi-utility models and Harsanyi's (1978) impartial-observer model. But the attempt to simply reinterpret choice-theoretic risk or uncertainty (in von-Neumann-Morgenstern's or Savage's framework) normatively runs into formal problems. For instance, writing 'normative information' into Savage's states implies letting states determine utilities, in ways not even compatible with standard statedependent utility theory (see however Riedener 2020 for a non-standard analysis). This would ultimately undermine the two-attitude make-up of choice theory, which is based on two independent ingredients, beliefs and values (or formally, probabilities and utilities). 2 But we here exclusively focus on Expectationalism. Our goal will not be to defend some version of Expectationalism against others, but to put the spectrum of expectational theories on the table. Deciding between expectational theories - choosing the right degree of ex-post-ness -might prove as diffi cult as deciding between ex-ante and expost egalitarianism. Although existing work has assumed a specific formulation of Expectationalism, it may be taken to address Expectationalism in general, because most arguments raised for or against Expectationalism do not hinge on that formulation. Our contribution is thus not to undermine existing arguments, but to raise the problem of the right formulation of Expectationalism. The paper has a simple structure. The rest of this section introduces the simplest possible formal framework for capturing normative and (where needed) empirical uncertainty. Section 2 introduces four salient expectational theories -four solutions to the problem 'expectation of what?'. Section 3 defines these four theories formally. Section 4 illustrates how they reach diverging evaluations and take different attitudes to risk. Section 5 defines Expectationalism in general, going beyond the four special theories. Section 6 concludes the paper. We now present the formal ingredients one by one. The objects of evaluation. We consider a non-empty set A of objects of evaluation, called 'options'. They could be policy measures, social arrangements, income distributions etc. So far we leave open whether options contain empirical risk. Competing valuations. Options have uncertain value. Following the expected-value approach, our notion of value is absolute, not comparative (ordinal). We thus represent a possible standard of evaluation by a function v, called a valuation, assigning to each option a in A a value v(a) in R. The agent hesitates between certain valuations. Let V be their set, formally a finite non-empty set of functions from A to R. Let us give moral examples, without suggesting a restriction to moral choice. V could contain a utilitarian, an egalitarian, and some deontological valuation. Alternatively, V could consist of 'similar'valuations which differ only in a parameter: prioritarian valuations with different degrees of prioritarianism, or egalitarian valuations with different degrees of inequality-aversion, or valuations of intertemporal well-being with different discounting of future well-being, or valuations (of risky options) with different degrees of risk-aversion, etc. In such parametric examples, normative uncertainty boils down to uncertainty about the correct parameter value: the correct amount of prioritarianism, inequality-aversion, discounting, risk-aversion, etc.3 Credences in valuations. You assign to each valuation v in V a subjective correctness probability Pr(v) ≥ 0, where ∑ v∈V Pr(v) = 1. Probabilities capture degrees of belief about value. 3Our examples show that normative uncertainty comes in two species: mere 'parameter uncertainty' and fundamental 'model uncertainty'. A similar distinction is made in other fields, especially statistics and macroeconomics (e.g., Hansen and Sargent 2001). 3 Meta-value. Given these credences, how should you evaluate options overall? An answer takes the form of a meta-valuation or theory, formally a function assigning to each option in A a (meta-)value in R. To distinguish meta-valuations from valuations, we denote them by upper-case letters like 'V '. Two examples suffi ce for now. Standard Expectationalism evaluates options a ∈ A by their expected value: V (a) =∑ v∈V Pr(v)v(a). Another (decidedly non-expectational) theory evaluates options a ∈ A by their minimal possible value: V (a) = minv∈V:Pr(v)6=0 v(a). Some would call our theories 'meta-theories', and our valuations 'first-order theories'. Nothing hinges on having defined theories as value functions rather than value orders on A; this is for convenience. Readers who prefer an ordinal notion of meta-value can replace our meta-valuations by the orders they induce. Measurability and comparability of value. As usual in the expectational approach, we take first-order value to be measurable and comparable across valuations. Full measurability makes it meaningful to say that an option x has value 7 under a valuation v (v(x) = 7), or is twice as valuable as another option y (v(x) = 2v(y)), or exceeds z's value by 2 (v(x) − v(z) = 2), etc. Full comparability makes it meaningful to say that two valuations v and v′ assign same value to option x (v(x) = v′(x)), or same value gain to the change from option x to option y (v(y)− v(x) = v′(y)− v′(x)), etc.4 Such assumptions are strong and debatable. They can be relaxed, in ways that differ across versions of Expectationalism.5 We set aside when and how measurability and comparability can be justified,6 and how they could be relaxed by different versions of Expectationalism. Adding empirical uncertainty: options as lotteries. The above framework is complete as a model of purely normative uncertainty; empirical uncertainty in options is allowed, but not modelled. To add empirical uncertainty explicitly, we hereafter assume that options in A are lotteries on a given set X of outcomes, i.e., functions a from X to [0, 1] such that ∑ x∈X a(x) = 1, where a(x) is non-zero for only finitely many x in X. An option is riskless if some outcome has probability one, and risky otherwise. Outcomes represent empirical states of affairs after resolution of empirical uncertainty. They may be 'consequences'of actions or go beyond 'consequences', depending on what we wish to model. Interpreting outcomes as consequences limits us to consequentialist valuations, hence to normative uncertainty between types of consequentialism. But if outcomes go beyond consequences, e.g., by capturing intentions or the choice context, then the model is open to non-consequentialist valuations, hence to normative uncertainty between possibly non-consequentialist valuations.7 We do not require that all lotteries on X count as options, i.e., belong to A. But 4Comparability and measurability are addressed by Bossert and Weymark (2004), and in the context of normative uncertainty by, e.g., Ross (2006), Sepielli (2009) and Tarsney (2018b). 5For instance, all versions need only affi ne measurements of value, and Standard Expectationalism needs only unit comparisons, not level comparisons. 6Justifying cross-valuation comparisons is easier if V consists of theories of similar type, e.g., egalitarian theories with different degrees of inequality-aversion. 7Normative uncertainty between non-consequentialist valuations is addressed by Barry and Tomlin (2016) and Tenenbaum (2017). 4 we assume that A contains at least the riskless lotteries, which assign probability one to some outcome. We take valuations v in V to also evaluate outcomes x in X, by defining v(x) as v(a) where a is the riskless option corresponding to x. Valuations of vNM type and of non-vNM type. A valuation v in V could have the notorious von-Neumann-Morgenstern property: it could 'be vNM'. Being vNM means evaluating options a in A by the expected value of the outcome: v(a) = ∑ x∈X a(x)v(x). Ever since the Harsanyi-Sen debate, it is controversial whether the vNM property is an arbitrary or an inherently necessary feature of 'value'.8 Our model is ecumenical: the agent could be utterly certain of the 'vNM Hypothesis', by having positive credence only in vNM valuations in V; or utterly certain of the converse, by having positive credence only in non-vNM valuations in V; or genuinely uncertain, by having positive credence in both types of valuations. Being ecumenical is important because we (as modellers) should not project our own view w.r.t. the vNM Hypothesis upon the agent to be modelled. We should for instance not force V to contain only vNM valuations or only non-vNM valuations, because this would restrict the entire framework to agents who are utterly certain of the vNM Hypothesis or of its converse, thereby excluding an important kind of normative uncertainty, namely uncertainty about the vNM Hypothesis. There is a deeper methodological point: the field or theory of normative uncertainty is engaged in meta-normativity and should thus avoid prejudging firstorder normative questions. It should take people's normative beliefs and uncertainties at face value (without 'forbidding'some of them), and tell people how to respond to their own uncertainties. Earlier work about normative uncertainty has often restricted attention to vNM valuations. This can be legitimate as a working assumption, but we wish to overcome this restriction. 2 Expected value of what? Expectational theories evaluate options by the expected value of some object. That object is the prospect offered by the option, but there are different types of prospect: the ex-ante prospect, the ex-post prospect, and hybrid prospects taking a partly ex-ante and partly ex-post perspective. Think of prospects as probability distributions. More precisely, one can define prospects equivalently as distributions over empirical-normative worlds ('world prospects') or distributions over resulting value levels ('value prospects'). We shall later only work with value prospects. But let us start with world prospects. An empirical-normative world -for short, a world -is a pair (x, v) of an outcome in X (an 'empirical world') and a valuation in V (a 'normative world'). In a world, all empirical or normative uncertainty is resolved. A world prospect is a probability distribution over worlds, representing how likely worlds are (where for simplicity only finitely many worlds have non-zero probability). Each option a generates an (ex-ante) world prospect, under which the probability of a world (x, v) is the product a(x)Pr(v) of the probabilities of 8See Broome (1991), Weymark (1991), Nissan-Rozen (2015) and Greaves (2017) for analyses with deviating conclusions. 5 outcome x (under option a) and valuation v. This world prospect is ex-ante because no uncertainty is resolved; ex-post and hybrid world prospects will be defined in a moment. We can now give four possible answers to the question 'Expected value of what?', hence four ways to reason and ultimately to assign meta-value to a given option a. We keep the answers informal; formal definitions follow in Section 3. • Normatively ex-post reasoning : You place yourself in a normatively ex-post and empirically ex-ante position, by considering a given valuation v and the lottery of empirical outcomes generated by option a. So you face the normatively ex-post world prospect, in which v has (marginal) probability one and any outcome x in X has (marginal) probability a(x). It yields the value v(a). Stepping outside this position, you then form the expectation of the value v(a) across valuations v in V. This is Standard Expectationalism. • Ex-post reasoning : You place yourself in a fully ex-post position, by considering a given outcome x and a given valuation v. So you face the ex-post world prospect, in which world (x, v) has probability one. It yields the value v(x). Stepping outside this position, you then form the expectation of the value v(x) across worlds (x, v) in X × V. This is Ex-Post Expectationalism. • Ex-ante reasoning : You place yourself in the fully ex-ante position, in which both parts of the empirical-normative world are unknown. So you face the ex-ante world prospect, defined above. You then form the expected value of this ex-ante prospect; how this works is shown in Section 3. This is Ex-Ante Expectationalism. • Empirically ex-post reasoning : You place yourself in an empirically ex-post and normatively ex-ante position, by considering a given outcome x and the probability distribution over valuations Pr reflecting your normative uncertainty. So you face the empirically ex-post world prospect, in which x has (marginal) probability of one and any valuation v has (marginal) probability Pr(v). You then form the expected value of this world prospect, in a way shown in Section 3. This is Reverse Expectationalism. It is the reverse or 'dual'of Standard Expectationalism, as it reasons ex-ante where Standard Expectationalism reasons ex-post, and vice versa. normatively ex-post normatively ex-ante empirically ex-post Ex-Post Expectationalism Reverse Expectationalism empirically ex-ante Standard Expectationalism Ex-Ante Expectationalism Table 1: Four expectational theories and their modes of reasoning These four answers to the question 'Expected value of what?'were given in the form of world prospects, but they can be redescribed as value prospects. Value prospects are prospects of achieving certain value levels (not worlds) with certain probabilities, for instance achieving value 4 with probability 1/2 and value 0 with probability 1/2. A world prospect immediately induces a value prospect (mathematically by taking the image of the world prospect under the mapping (x, v) 7→ v(x) from worlds to resulting values). For instance, the ex-post world prospect under which world (x, v) is certain induces the riskless value prospect under which the value v(x) is certain. Formally, a value prospect is simply a lottery over real numbers, i.e., a function p 6 assigning to each value k in R a probability p(k) in [0, 1] such that ∑ k∈R p(k) = 1, where (for simplicity) only finitely many values k in R have non-zero probability p(k). Each option a generates a value prospect, denoted pa. It reflects empirical and normative uncertainty, as the resulting value v(x) depends on both x and v, hence on the empiricalnormative world (x, v). The probability that the resulting value is (say) 4 is the sumtotal probability of all worlds (x, v) such that v(x) = 4. The just-defined value prospect pa of an option a is an ex-ante construct: no uncertainty is yet resolved. Indeed, pa is simply the value prospect induced by the ex-ante world prospect. Partly or fully ex-post value prospects are definable by eliminating one or both sources of uncertainty. We now formally define the four kinds of value prospect. They correspond exactly to the four kinds of world prospect above, respectively:9 • The (ex-ante) value prospect of option a ∈ A is the value prospect 'pa'such that any value k ∈ R has probability pa(k) = 'probability that a leads to value k'= ∑ (x,v)∈X×V:v(x)=k a(x)Pr(v)} {{ } prob. of (x,v) . • The (normatively ex-post) value prospect of option a ∈ A given valuation v ∈ V is the value prospect 'pa,v'such that any value k ∈ R has probability pa,v(k) = 'probability that a leads to value k given v' = ∑ x∈X:v(x)=k a(x). • The (empirically ex-post) value prospect given outcome x ∈ X is the value prospect 'px'such that any value k ∈ R has probability: px(k) = 'probability that x leads to value k' = ∑ v∈V:v(x)=k Pr(v). • The (ex-post) value prospect given both x ∈ X and v ∈ V is the riskless value prospect 'px,v'under which the value is v(x) with probability one.10 3 Four expectational theories We now formally define the four expectational theories discussed in Section 2. Each takes the expected value of a certain prospect, as is either clear by definition or established in Theorem 1. We begin with the two theories whose definitions do not explicitly refer to prospects. Standard Expectationalism ('EVstan'): The meta-value of an option a ∈ A is the expected value of the option itself: EVstan(a) = ∑ v∈V Pr(v)v(a) ('standard expected value'). 9Compare our value prospects with Rowe and Voorhoeve's (2018) well-being prospects in a context of health ethics under (purely empirical) risk, uncertainty, or ambiguity. 10The value prospects px and px,v can be regarded as special cases of the value prospects pa and pa,v, by choosing a to be the riskless option that yields x for sure. 7 This theory reasons empirically ex-ante, because the object whose average evaluation it forms is the option, which captures empirical risk. The second theory reasons fully ex-post: it forms the average evaluation of the outcome, which no longer contains empirical risk. This requires averaging across both outcomes and valuations, hence across empirical-normative worlds (x, v). Formally: Ex-Post Expectationalism ('EVpost'): The meta-value of an option a ∈ A is the expected value of the outcome: EVpost(a) = ∑ (x,v)∈X×V a(x)Pr(v)} {{ } prob. of (x,v) v(x) ('expected final value'). The third theory reasons fully ex-ante. It operates neither at the ex-post level of outcomes (EVpost), nor at the semi-ex-post level of options (EVstan), but at the level of ex-ante value prospects. But how can valuations v in V evaluate value prospects rather than options, i.e., how should we define v(p) for a value prospect p? We of course identify v(p) with v(a) for any option a in A chosen such as to have the value prospect p given v. If for instance p is the value prospect 'the value is 1 or 0 equiprobably', then we pick an option a which equiprobably has an outcome x of value v(x) = 1 or an outcome y of value v(y) = 0, and define v(p) as v(a). Formally, the value of a value prospect p under a valuation v in V -denoted v(p) -is the value v(a) of options a ∈ A such that pa,v = p. This definition implicitly rests on an assumption that we shall maintain for the rest of the paper: Assumption: Hereafter, for each valuation v in V and value prospect p we assume that (i) A contains an option a whose value prospect given v, pa,v, is p; and (ii) any two such options a in A have same value v(a). Condition (i) is a typical richness assumption: the set of options A should be suffi ciently inclusive, i.e., contain options with any given value prospects. Condition (ii) is a consistency assumption on the valuations in V. It is compatible with most or all first-order theories one would naturally want to consider. Ex-Ante Expectationalism ('EVante'): The meta-value of an option a ∈ A is the expected value of the ex-ante prospect: EVante(a) = ∑ v∈V Pr(v)v(pa) ('expected ex-ante value'). Note an intended peculiarity: v(pa) uses a given valuation (v) to evaluate a prospect (pa) which carries normative uncertainty about the correct valuation. Precisely this is what ex-ante reasoning should do, as it should ask how attractive each ex-ante prospect is on average across possible valuations. The fourth theory calculates the average evaluation of yet another object: neither the option (EVstan), nor the outcome (EVpost), nor the ex-ante prospect (EVante), but the empirically ex-post value prospect. This requires averaging across outcomes and valuations, hence across empirical-normative worlds (x, v). 8 Reverse Expectationalism ('EVrev'): The meta-value of an option a ∈ A is the expected value of the empirically ex-post prospect: EVrev(a) = ∑ (x,v)∈X×V a(x)Pr(v)} {{ } prob. of (x,v) v(px) ('reverse expected value'). This theory reverses the reasoning of Standard Expectationalism: it reasons empirically ex-post rather than normatively ex-post. The following theorem re-expresses the four theories in a comparable format, showing that they only differ in the 'locus'of expectation-taking, i.e., in the sort of prospect whose expected value they maximise: Theorem 1 Each expectational theory V ∈ {EVante, EVpost, EVstan, EVrev} evaluates any option a ∈ A by the expected value of a specific value prospect, i.e., V (a) = ∑ (x,v)∈X×V a(x)Pr(v)} {{ } prob. of (x,v) v(p), where 'p'stands for the • ex-ante value prospect pa if V = EVante, • ex-post value prospect px,v if V = EVpost, • normatively ex-post value prospect pa,v if V = EVstan, • empirically ex-post value prospect px if V = EVrev. 4 Illustration of these expectational theories and their risk attitudes Suppose you hesitate between just two valuations, v and v′. You have credence 12 in each of them, and credence 0 in all other valuations in V (if any). Both valuations v and v′ are risk-averse. So you are sure that risk-aversion is correct: your normative uncertainty does not pertain to the risk-attitude (more on risk attitudes in the next section). You now compare two options. Both options lead to the value prospect 'value 4 with probability 12 , value 0 with probability 1 2', denoted 450%050%, but for very different reasons. The first option involves only normative risk: it surely has outcome x, whose value is either v(x) = 4 or v′(x) = 0. The second option involves only empirical risk: it has either outcome y or outcome z (equiprobably), where it is uncontroversial between v and v′ that y has value 4 and z has value 0. By risk-aversion, the option is evaluated below the expected resulting value of 124 + 1 20 = 2; let the value be 1 under both v and v′. The gap from 1 to 2 is a 'risk penalty'or 'risk premium'. Table 2 displays the (ex-ante and normatively ex-post) value prospects of options and the evaluations of options by both first-order theories and the four expectational theories. The four meta-evaluations are obtained as follows: • Standard Expectationalism forms the average value of the option. This yields 1 24 + 1 20 = 2 or 1 21 + 1 21 = 1, respectively. 9 value prospect evaluation by given v given v′ ex-ante v v′ EVstan EVpost EVante EVrev option 1 4100% 0100% 450%050% 4 0 2 2 1 1 option 2 450%050% 450%050% 450%050% 1 1 1 2 1 2 Table 2: Four expectational theories applied to two concrete options • Ex-Post Expectationalism forms the average value of the outcome. In principle, this requires averaging across valuations in V (normative uncertainty) and outcomes (empirical uncertainty). Yet our options effectively need just one dimension of averaging, as they have just one source of uncertainty. The first option has just normative uncertainty: it surely has outcome x, of value 4 or 0. The second option has just empirical uncertainty: it has outcome y of sure value 4 or outcome z of sure value 0. Each option thus has the same average value of the outcome: 1 24 + 1 20 = 2. • Ex-Ante Expectationalism forms the average value of the ex-ante value prospect, i.e., of 450%050% for each option. So we must calculate 1 2v(450%050%) + 1 2v ′(450%050%). What are v(450%050%) and v′(450%050%)? As 450%050% is option 2's value prospect given v, v(450%050%) = v(option 2) = 1. As 450%050% is also option 2's value prospect given v′, v′(450%050%) = v′(option 2) = 1. So, 1 2v(450%050%) + 1 2v ′(450%050%) = 1 21 + 1 21 = 1. • Reverse Expectationalism forms the average value of the empirically ex-post value prospect. Like for EVpost, this can require averaging across both outcomes and valuations, but for our two options one dimension of averaging drops out, as option 1 is empirically riskless and option 2 is normatively riskless. Option 1 surely has outcome x, whose value prospect 450%050% is evaluated at 1 by both (risk-averse) valuations, as just seen. The average value is thus 121 + 1 21 = 1. Option 2 either has outcome y, whose value prospect 4100% has value 4 under both v and v′; or has outcome z, whose value prospect 0100% has value 0 under both v and v′. The average value is thus 124 + 1 20 = 2. Interestingly, the reasoning mode -the version of Expectationalism adopted -determines the risk attitude underlying our (meta-)evaluations. We shall explain this only informally; the relevant definitions and formal results are given in a separate paper dedicated to risk attitudes (Dietrich and Jabarian 2019b). By 'risk'we mean risk about resulting value, such as (in our example) the risk of achieving value 4 or 0. Such risk can have empirical or normative origin: it can stem from an uncertain outcome (empirical uncertainty) or an uncertain value of the outcome (normative uncertainty) or even a combination. A (meta-)theory is risk-averse if its evaluation of options contains a penalty for risk, i.e., falls below the value achieved in expectation. The theory is risk-neutral if risk is not penalized. One might feel uncomfortable with imposing a particular risk attitude on meta-evaluations: where would the justification come from? An interesting alternative to imposition is to adopt whatever risk attitude you believe to be correct for first-order valuations: if you are certain of a particular risk attitude such as risk-aversion, i.e., hold positive credence only in valuations with that risk-attitude, then your meta-evaluations adopt that same risk-attitude. We call this risk-impartiality 10 because your meta-level risk attitude defers to your risk-attitudinal judgments. In our example, you are certain that risk-aversion is correct, as you are certain that one of the risk-averse valuations v and v′ is correct; so a risk-impartial theory is risk-averse. Risk-impartiality seems to be a natural default, at least in the absence of a convincing argument for any particular meta-level risk attitudes (see Dietrich and Jabarian 2019b for risk-impartiality in cases of not being certain of any given risk-attitude).11 Which risk-attitudes do our four theories have? As summarized in Table 3, one is risk-neutral (no penalty for risk), one is risk-impartial (deference to risk-attitudinal judgments), and two have hybrid risk-attitudes, i.e., are risk-neutral or risk-impartial depending on the origin of risk. To explain why, we use our example, in which all neutral to normative risk impartial to normative risk neutral to empirical risk Ex-Post Expectationalism Reverse Expectationalism impartial to empirical risk Standard Expectationalism Ex-Ante Expectationalism Table 3: The risk attitudes of the four meta-theories in our example with risk-averse first-order theories valuations are risk-averse, so that risk-impartiality becomes risk-aversion. • Standard Expectationalism applies the valuations v and v′ to the option, which captures only empirical risk. This leads (by risk-aversion of v and v′) to a penalty or discount for empirical risk only: the theory is averse to empirical risk, but neutral to normative risk. This explains why in Table 2 the normatively risky option 1 gets the undiscounted value of 2, while the empirically risky option 2 gets the discounted value of 1. • Ex-Post Expectationalism applies the two valuations to the outcome, which captures no risk. So no risk is penalized: the theory is globally risk-neutral. This explains why both options in Table 2 get the undiscounted value of 2. • Ex-Ante Expectationalism applies the two valuations to the ex-ante value prospect, which captures risk of both origins. So all risk is penalized: the theory is globally risk-averse. This explains why both options in Table 2 get the discounted value of 1. • Reverse Expectationalism applies the two valuations to the empirically ex-post value prospect, which captures only normative risk. So only normative risk is penalized: the theory is averse only to normative risk. This explains why in Table 2 only the normatively risky option gets the discounted value of 1. 5 The full taxonomy of Expectationalism We now come to the unification. We introduce a single generic expectational theory, of which our four earlier theories are nothing but special cases. The generic theory depends on a parameter that determines the reasoning mode, i.e., the extent of expost-ness. Particular choices of this parameter yield our four special expectational 11Risk attitudes have been analysed extensively in the different context of purely empirical uncertainty. For different accounts, see Weirich (1986), Buchak (2013), Bradley and Stefánsson (2017) and Baccelli (2018). 11 theories, and all other expectational theories. So there are not just four expectational theories, but a large and unified class of expectational theories. The parameter determining the expectational theory is the type of information relative to which reasoning is ex-post: full information yields Ex-Post Expectationalism, no information yields Ex-Ante Expectationalism, purely normative information yields Standard Expectationalism, purely empirical information yields Reverse Expectationalism, and yet other types of information yield other expectational theories. We model an information by an empirical-normative event I ⊆ X × V, containing the empiricalnormative worlds (x, v) which are consistent with the information. The information of a full empirical-normative world (x, v) is I = {(x, v)}, containing just one world; the vacuous or tautological information is I = X×V, containing all worlds; the information of a valuation v is I = X×{v}, containing worlds of type (∗, v); and the information of an outcome x is I = {x} × V, containing worlds of type (x, ∗). Recall that each option a generates a world prospect, i.e., a probability function over worlds. Let us denote it by Pa. The probability of a world (x, v) is Pa(x, v) = a(x)Pr(v), the product of the probabilities of x and v. To define our general expectational theory, we need a general notion of value prospect, which has an arbitrary degree of ex-post-ness, i.e., conditionalises on an arbitrary information I. We call it the 'ex-I value prospect', as it is the value prospect from the perspective of I. Formally, for any option a ∈ X and information I ⊆ X × V (of non-zero probability Pa(I)), the ex-I value prospect of a is the value prospect pa,I such that the probability of a value level k ∈ R is the probability that a results in value k given I: pa,I(k) = probability of final value k given I = prob. of [I & final value k] prob. of I = Pa({(x, v) ∈ I : v(x) = k}) Pa(I) . This general notion of value prospect encompasses our four earlier notions: Proposition 1 The ex-I value prospect pa,I of an option a ∈ A given an information I ⊆ X × V (of non-zero probability Pa(I)) coincides with the • ex-ante value prospect pa if I = X × V (no information), • ex-post value prospect px,v if I = {(x, v)} (information of a full world (x, v)), • normatively ex-post value prospect pa,v if I = X×{v} (information of a valuation v), • empirically ex-post value prospect px if I = {x}×V (information of an empirical outcome x). Recall that each valuation v in V can evaluate not just options, but also value prospects. So we can form v(pa,I), which tells how valuable v finds the prospect of option a given I. We call v(pa,I) a's ex-I value of a, according to v. An expectational theory reasons ex-post w.r.t. some type of information. A type of information is represented by an information partition: a partition I of the set X × V of empirical-normative worlds. I contains those information I on which the 12 reasoner conditionalises when conceptualizing options as prospects. As such, I defines a degree of ex-post-ness of reasoning. Fully ex-post reasoning is defined by the finest information partition I = {{(x, v)} : (x, v) ∈ X × V}; fully ex-ante reasoning by the coarsest partition I = {X × V}; normatively ex-post reasoning by the partition I = {X × {v} : v ∈ V} into 'valuation events'; empirically ex-post reasoning by the partition I = {{x} × V : x ∈ X} into 'outcome events'; and other hybrid reasoning modes by other partitions. An information partition I -a degree of ex-post-ness -determines an expectational theory, which evaluates options by the expected value (across empirical-normative worlds) of the prospect w.r.t. I. Formally: Ex-I Expectationalism ('EVI'): The meta-value of an option a ∈ A is the expected value of the ex-I prospect:12 EVI(a) = ∑ (x,v)∈X×V a(x)Pr(v)} {{ } prob. of (x,v) v(pa,I(x,v)) ('expected ex-I value') where I(x, v) is the information in empirical-normative world (x, v), i.e., the I ∈ I containing (x, v). We can now define 'Expectationalism'as a general approach: Expectationalism: Meta-value is given by some expectational theory, i.e., by Ex-I Expectationalism for some information type I (some partition of X × V). Our four earlier theories are special cases, obtained by plugging in certain information types, i.e., certain degrees of ex-post reasoning: Theorem 2 Ex-I Expectationalism coincides with • Ex-Ante Expectationalism if I = {X × V} (no information), • Ex-Post Expectationalism if I = {{(x, v)} : (x, v) ∈ X × V} (full information), • Standard Expectationalism if I = {X × {v} : v ∈ V} (normative information), • Reverse Expectationalism if I = {{x} × V : x ∈ X} (empirical information). Are there any circumstances under which it becomes irrelevant how you reason? That is, can it happen that all degrees of ex-post reasoning extensionally yield the same expectational theory, hence the same evaluative judgments, albeit through different procedures? This question obviously matters. If all reasoning modes were extensionally equivalent, then you could reason as you wish or find easiest. The question has a sharp answer: the reasoning mode is irrelevant if and only if you have full credence in the vNM Hypothesis, i.e., assign zero probability to all valuations in V that are not vNM ('von-Neumann-Morgenstern'). Recall that vNM valuations v evaluate options a in A by the expected value of the outcome: v(a) = ∑ x∈X a(x)v(x). See Section 1 for discussion. 12Although pa,I(x,v) becomes undefined in the zero-probability case Pa(I(x, v)) = 0, no ambiguity arises. Whenever pa,I(x,v) is undefined, the value v(pa,I(x,v)) can be interpreted arbitrarily, as it is multiplied by 0 (= Pa(x, v) = a(x)Pr(v)) and so has no effect. 13 Theorem 3 All expectational theories EVI coincide (i.e., the reasoning mode has no effect) if and only if you are certain of the vNM Hypothesis, i.e., Pr(v) = 0 for all valuations v in V that are not vNM. Some scholars have defended the vNM Hypothesis (and many have assumed it to simplify models). But few of them would go so far as to be utterly certain of that hypothesis. These few people can safely reason as they wish: their reasoning mode has no effect by Theorem 3. All others, who have at least some doubt in the vNM Hypothesis, face the hard choice between reasoning modes, i.e., between expectational theories. 6 Conclusion There is more than one expected-value theory for evaluating options. The various expectational theories differ ethically, by reaching different evaluations; procedurally, by using different reasoning; and risk-attitudinally, by taking different attitudes towards empirical as well as normative risk. But all theories take the expected value of some type of prospect. At the two ends of the spectrum, Ex-Ante and Ex-Post Expectationalism respectively take the expected value of the ex-ante or ex-post prospect, hence reason from the perspective before or after resolution of uncertainty of any type (empirical or normative). Standard Expectationalism lies in between: it takes the expected value of the option itself, thereby effectively reasoning from an empirically ex-ante, but normatively ex-post perspective. Reverse Expectationalism does the opposite: it reasons empirically ex-post, but normatively ex-ante. The four mentioned theories stand out as salient, but they are just examples. In general, to any type of information (technically, any 'information partition'of the set of empirical-normative worlds) corresponds an expectational theory, which reasons ex-post w.r.t. this information. The classical question 'Expectationalism or not?' should therefore be complemented by another pressing question: 'Expected value of what?' The problem of deciding between versions of Expectationalism might prove to be as diffi cult as the classic problem of deciding between ex-ante and ex-post versions of egalitarianism -or perhaps even more diffi cult, as we face normative uncertainty besides empirical uncertainty. 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Secondly, EVstan(a) = ∑ v∈V Pr(v)v(a) = ∑ v∈V Pr(v)v(pa,v) = ∑ v∈V Pr(v)v(pa,v) ∑ x∈X a(x) = ∑ (x,v)∈X×V a(x)Pr(v)v(pa,v), where the second equality holds because v(pa,x) = v(a), and the third because ∑ x∈X a(x) = 1. Thirdly, the expression for EVrev(a) holds by definition. Finally, EVpost(a) = ∑ (x,v)∈X×V a(x)Pr(v) v(x)}{{} =v(px,v) = ∑ (x,v)∈X×V a(x)Pr(v)v(px,v).  Proof of Proposition 1. Consider an option a ∈ A and an information I ⊆ X × V such that Pa(I) 6= 0. As our definitions easily imply, if I = X × V then pa,I = pa, while if 16 I = {(x, v)} where (x, v) ∈ X × V then pa,I = px,v. If I = X × {v} where v ∈ V, then pa,I = pa,v because for all k ∈ R pa,I(k) = Pa({x ∈ X : v(x) = k} × {v}) Pr(v) = a({x ∈ X : v(x) = k})Pr(v) Pr(v) = a({x ∈ X : v(x) = k}) = ∑ x∈X:v(x)=k a(x) = pa,v(k). Finally, if I = {x} × V where x ∈ X, then pa,I = px because for all k ∈ R pa,I(k) = Pa({x} × {v ∈ V : v(x) = k}) a(x) = a(x)Pr({v ∈ V : v(x) = k}) a(x) = Pr({v ∈ V : v(x) = k}) = ∑ v∈V:v(x)=k Pr(v) = px(k).  Proof of Theorem 2. Regarding EVante, for each option a ∈ A EVante(a) = ∑ (x,v)∈X×V a(x)Pr(v)v(pa) = EVI(a) for I = {X × V}. where the first identity holds by Theorem 1 and the second identity holds because by Proposition 1 we can replace pa by pa,X×V = pa,I(x,v). Analogously, for each a ∈ A EVstan(a) = ∑ (x,v)∈X×V a(x)Pr(v)v( pa}{{} pa,X×{v} ) = EVI(a) for I = {X × {v} : v ∈ V} EVrev(a) = ∑ (x,v)∈X×V a(x)Pr(v)v( px}{{} pa,{x}×V ) = EVI(a) for I = {{x} × V : x ∈ X} EVpost(a) = ∑ (x,v)∈X×V a(x)Pr(v)v( px,v}{{} pa,{(x,v)} ) = EVI(a) for I = {{(x, v)} : (x, v) ∈ X × V}, where on each line the two identities use Theorem 1 and Proposition 1, respectively.  The proof of Theorem 3 begins with a lemma. Lemma 1 A valuation v ∈ V is vNM if and only if it evaluates value prospects by their expectation, i.e., v(p) = Exp(p) (= ∑ k∈R p(k)k) for all value prospects p. Proof. 1. First, let v ∈ V be vNM. We fix a value prospect p and prove that v(p) = Exp(p). Pick an option a ∈ A such that pa,v = p. We have Exp(p) = ∑ k∈R kp(k) = ∑ k∈R k ∑ x∈X:v(x)=k a(x) = ∑ k∈R ∑ x∈X:v(x)=k a(x)k = ∑ x∈X a(x)v(x), where the second equality uses that p(k) = pa,v(k) = ∑ x∈X:v(x)=k a(x), and the third and fourth equalities follow by reordering terms. The last expression equals v(a) as v is vNM, which equals v(p) by choice of a. 2. Conversely, assume v(p) = Exp(p) for all value prospects p. We let a ∈ A and show v(a) = ∑ x∈X a(x)v(x). Defining p as pa,v, we have Exp(p) = ∑ x∈X a(x)v(x), 17 as in part 1 of the proof. So it remains to show v(a) = Exp(p). This holds because v(a) = v(p) (as p = pa,v) and v(p) = Exp(p) (by hypothesis).  Proof Theorem 3. We shall use standard measure-theoretic arguments. 1. Assume Pr(v) = 0 for all non-vNM valuations v ∈ V. Fix an option a ∈ A. We show that EVI(a) is independent of the information partition I. On the set of worlds X×V, consider the probability distribution Pa (the world prospect of a) and the random variables x : X × V → X, (x, v) 7→ x and v : X × V → V, (x, v) 7→ v. Combining these variables yields a third variable, v(x), given by X × V → R, (x, v) 7→ v(x) and representing resulting value. The value prospect pa equals the distribution of the variable v(x), and so its expectation is Exp(pa) = ExpPa(v(x)). More generally, for any information I ⊆ X × V (such that Pa(I) 6= 0), the value prospect pa,I equals the distribution of v(x) conditional on I, and so Exp(pa,I) = ExpPa(v(x)|I). Now for any information partition I (identifiable with the variable mapping (x, v) to I(x, v)), EVI(a) = ExpPa(v(pa,I)) by definition = ExpPa(Exp(pa,I)) by Lemma 1 = ExpPa(ExpPa(v(x)|I)) as Exp(pa,I) = ExpPa(v(x)|I) = ExpPa(v(x)) by the law of iterated expectations, where Lemma 1 is applicable as valuations generated by v (with non-zero probability) are vNM. The last expression for EVI(a) shows that EVI(a) is independent of I. 2. Conversely, let V contain a non-vNM valuation ṽ of probability Pr(ṽ) 6= 0. As ṽ is non-vNM, we may pick an option a ∈ A such that ṽ(a) 6= ∑ x∈X a(x)ṽ(x). Denote the information of valuation ṽ by I = X × {ṽ}. We construct two information partitions I1 and I2 for which EVI1(a) 6= EVI2(a). Let I1 and I2 coincide outside I and be, respectively, maximally coarse or maximally fine within I. So I1 = I0 ∪ {I} and I2 = I0 ∪ {{(x, v)} : (x, v) ∈ I}, for some partition I0 of (X × V)\I. Thus EVI1(a) = S + S1 and EVI2(a) = S + S2 where S = ∑ (x,v)∈(X×V)\I a(x)Pr(v)v(pa,I0(x,v))) S1 = ∑ (x,v)∈I a(x)Pr(v)v(pa,I1(x,v))) = ∑ x∈X a(x)Pr(ṽ)ṽ(pa,I) S2 = ∑ (x,v)∈I a(x)Pr(v)v(pa,I2(x,v))) = ∑ x∈X a(x)Pr(ṽ)ṽ(pa,{(x,ṽ)}). By Proposition 1, pa,I = pa,ṽ and pa,{(x,ṽ)} = px,ṽ. So ṽ(pa,I) = ṽ(pa,ṽ) = ṽ(a) and ṽ(pa,{(x,ṽ)}) = ṽ(px,ṽ) = ṽ(x). Thus S1 = ∑ x∈X a(x)Pr(ṽ)ṽ(a) = Pr(ṽ)ṽ(a) ∑ x∈X a(x) = Pr(ṽ)ṽ(a) S2 = ∑ x∈X a(x)Pr(ṽ)ṽ(x) = Pr(ṽ) ∑ x∈X a(x)ṽ(x). So EVI1(a)− EVI2(a) = S1 − S2 = Pr(ṽ) ( ṽ(a)− ∑ x∈X a(x)ṽ(x) ) . As Pr(ṽ) 6= 0 and ṽ(a) 6= ∑ x∈X a(x)ṽ(x), we deduce EVI1(a) 6= EVI2(a). 