The Tyranny of the Ideal
Page 9
(i∼k)] ⇔ d(i, j) < d(i, k)].29
Figure 2-1. Orderings and metrics
With these last two features of a perspective (the complete similarity ordering and distance metric) we now can fully meet the Orientation Condition. A perspective containing the final two elements can generate an overall evaluation of nonideal members of {X}, not simply by referring to their individual justice scores (as the Social Realizations Condition requires), but also by their similarity-distance to the ideal social world, u, which is a member of {X}. Social world u, we can say, is the global optimum in {X}—it has the greatest justice score. But the distance metric implies that some world i, whose justice score is close to that of u might not be close in this second sense—its underlying structure could be far from u’s. Once again, it is important to stress that introducing the distance metric is by no means an artificial idea simply motivated by my statement of the Orientation Condition. If we seek to understand how our social world is similar to others, and which are most similar to the ideal, and this is not to collapse into Sen’s nonideal climbing model (§I.1.3), some understanding of overall structural similarity is critical, and distance metrics are standard in modeling the similarity (or diversity) of different elements in a given domain (e.g., ecosystems).
We now have specified five elements of any evaluative perspective suited to ideal justice:
(ES) A set of evaluative standards;
(WF) An identification of the relevant features of social worlds;
(MP) A mapping relation from evaluative standards to the justice-relevant features of social worlds, yielding a justice score for each social world;
(SO) An ordering of the underlying structures as identified in WF that relates the social worlds in {X} in terms of a complete, consistent similarity ordering;
(DM) A distance metric applied to this similarity ordering.
Together ES, WF, and MP satisfy the Social Realizations Condition, while SO and DM satisfy the Orientation Condition.
1.3 Why Not Feasibility?
I have interpreted the Orientation Condition as requiring a second dimension of evaluation (in addition to the intrinsic justice of a social world), that of similarity of justice-relevant structures and background facts. No doubt some readers have been puzzled, assuming that this second dimension must be some sort of feasibility metric or space. Indeed, in Simmons’s rejoinder to Sen’s “climbing model,” he insists that a mere ordering of states of affairs in terms of their intrinsic justice will not suffice because, in addition to the height (justice score) of a location, we are interested in “feasible paths to the highest peak of perfect justice” (§I.1.3).30 So: why not feasibility rather than similarity?
I certainly do not deny that, in many respects and in many contexts, feasibility is critical. I have insisted that some notion of feasibility—no matter how optimistic—is always part of modeling a possible world and its justice (§I.3.1); because the mapping function includes such models of worlds, a notion of feasibility is intrinsic to our account of perspectives on justice. And, of course, to make sound action recommendations to a person, a political entity, or a collectivity, a different sort of feasibility consideration is critical—the feasibility of moves from one social world to another. The importance of feasibility in many contexts is undeniable.31 Our present concern however, is whether something like a “feasibility metric” can satisfy the Orientation Condition in ideal theories of justice.
I think it is quite clear that, while recent work in political theory has focused on feasibility as central to the “ideal/nonideal debate,” it is not an appropriate metric by which to satisfy the Orientation Condition. Gilabert and Lawford-Smith analyze feasibility as a four-placed predicate of the form: “It is feasible for X to ϕ to bring about O in Z,” where X is an agent, ϕ a set of actions, O a set of outcomes, and Z a context, which “might be broad, ranging over all of human history, or narrow, limited to a particular time and a particular place.”32 Notice that feasibility is indexed to agents, time spans, and contexts. Thus outcome O may be feasible for Alf at time t1 in circumstances C1, but not at time t2, but might be again feasible at t3, though now the circumstances have changed. Perhaps it was feasible for Betty only at t2 in circumstances C2. For a theory of the ideal to specify a plausible feasibility space to orient our quest for justice, it would have to specify not only the agent (for example, feasibility could be defined in terms of the US Congress, the American people, Western society), but a time period. Suppose then a theory specifies Z as “any time within the next decade” and X = “US Congress and president.” So only things that are feasible throughout the entire decade for the American Congress and president are in the feasibility space. But again, even this claim must be indexed to time. At the beginning of the decade, time t0 it may be true that (i) Congress along with the president can ϕ to achieve outcome O any time during the time span t0–t10. But what Congress can achieve any time in the decade is apt to change as the decade proceeds, and Congress and the president take other actions that impact on whether ϕ can still achieve outcome O. Thus at time t3, it may (ii) be false that Congress can ϕ to achieve outcome O during the remaining time span t3–t10, which would contradict claim (i), unless each is further indexed to a certain time for which it is true.
For example, in the American Civil War many in the North commenced the war to restore the union to something like its antebellum form, with slavery permitted in the southern states (but not federal territories). This was also the initial aim of Lincoln, and it certainly seemed feasible at the outbreak of the war; it is plausible to say that for most in the North the war was undertaken to achieve this end. But Lincoln decided that the Emancipation Proclamation was needed for successful prosecution of the war; after it, and after the years of bloody battles, it was very likely no longer feasible in 1863 that the union could be restored its antebellum form, even though the Democratic Party fought the 1864 election on a platform basically devoted to achieving it. In this case an aim O that was perfectly feasible in 1861 (and in 1861 would seem feasible throughout the next five years) became infeasible—interestingly, because of events that were instigated to achieve O, in 1864. Given the way feasibility changes as actors proceed through time, it seems important to always index feasibility judgments to the time of evaluation.33 But, of course, then “feasibility space” is highly unstable, and that seems inappropriate for a theory of justice.
Additionally, feasibility judgments have many different dimensions, which operate over differing time spans, agents, and contexts. Wiens nicely sketches the many different dimensions of feasibility:
We can make our assessment more tractable by grouping the relevant facts into analytically useful general categories. Some straightforwardly rigid constraints are logical consistency, the laws of nature and human biology. But we should also attend to less rigid, more malleable constraints. I only present a few salient examples here, leaving development of a full list as a practical exercise: ability constraints, which comprise facts about human abilities; cognitive constraints, which comprise facts about our cognitive capacity, including cognitive biases and computational limitations; economic constraints, which—if taken broadly—comprise facts about possible allocations of money, labour power and time; institutional constraints, which include facts about institutional structure and capacity (for example, the number and distribution of veto points in a collective decision procedure and the ways in which political officials are selected); and technological constraints, which include facts about the tools, techniques and organizational schemes available for bringing about new states of affairs. I [include] … motivational constraints, which identify the limits of what people can be motivated to do given intrinsic features of human agents that affect motivation (including affective biases, prejudices and fears), as well as the extrinsic features of an agent’s environment that interface with her intrinsic motivational capacities (including social norms and incentives).34
Wiens argues that different features can be aggr
egated into a general notion of feasibility (though he is somewhat skeptical about how accurate such general feasibility judgments might be).35 He proposes aggregating all these facets into an overall “feasibility frontier,” modeled on the economists’ “production possibility frontier.” According to Wiens, “we can say that a possible world is a member of the feasible set only if that world is circumstantially accessible from the actual world. … It follows that realizing a state is feasible only if there is at least one world at which the state is realized that is circumstantially accessible from the actual world; realizing the state is otherwise infeasible.”36 The idea of “circumstantially accessible” does not mean simply that no constraints exclude the move, but that given the resources and causal process obtaining in the present world, the feasible state of affairs can be brought about, much like the production possibility frontier tells us what level of production can be brought about in a given economy. Others are skeptical whether the many different layers of feasibility, differing according to time spans, and contexts, can be coherently brought together in this way.37 Again, even if all this could be done, feasibility judgments must be time and agent indexed. Because the conditions that underlie feasibility are constantly shifting, the landscape of justice would be as well. While we may have to take account of these constant shifts for policy purposes, such a landscape would hardly orient our thinking about justice, providing what Rawls called “a long-term goal of political endeavor” (§I.1.2).38
Perhaps, it might be thought, a feasibility space that could satisfy the Orientation Condition could be constructed with some more fixed notion of feasibility, say “social engineering feasibility”: social world i is close to j just in case “we” (the agents still must be defined) have a social technology that could (under some parameters) reform i into j. I shall not explore this idea. But note that any space defined in terms of feasibility will behave oddly. Recall (§II.1.2) that we adopted a constraint on distance metrics: d(i, j) = d(j, i), which requires that the distance between worlds i and j is the same as that between j and i. Our similarity metric (DM) satisfies this general condition. A feasibility-based similarity metric would not. As Geoffrey Brennan and his coauthors point out, some social states are “absorbing” in the sense that once a society is in that state, it may be very difficult to get out—such states are “sink holes.”39 Thus a move from i into the “sinkhole” j may be highly feasible (so, not distant in feasibility space), but moving back from j is not at all feasible, thus undermining symmetry: i is close to j, but j is not close to i. Note also that “feasible to move” is, at least in one sense, not a transitive relation: that it is feasible to move from i to j, and that it is feasible to move from j to k, does not mean that it is feasible to move directly from i to k; in fact that might be impossible.40 Jon Elster believes that a utopian theory must advocate such transitivity. “Utopianism says that what can be done in two steps can also be done in one step.”41 Elster thinks this is false on its face, but unless some sort of transitivity holds an ideal theory may well be committed to the much-dreaded “incrementalism”;42 in order to get to the ideal, the theory may require that we proceed through the worlds “on the way” to the ideal, in something like a step-by-step fashion. Moreover, the time indexicality of feasibility leads to other relations that look intransitive. If at time t, it is feasible to move from i to j, and at time t, it is feasible to move from j to k, it does not follow that at time t it is feasible to move from i to k, either directly (as noted above) or indirectly (because by the time we move from i to j, time t will no longer obtain, and we may not know whether a move from j to k is feasible at time t2). Whatever else may be the case with this complicated idea, feasibility space is certainly oddly behaved and shifting—indeed rather disorienting.
For a number of reasons, then, the Orientation Condition should not be interpreted in terms of feasibility. As Juha Räikkä observes, “To construct a political theory is not necessarily to engage in politics. … A political theory concerns the issue of what one is justified in thinking about the moral status of societal institutions, and it does not follow that what one is justified in thinking, one should do at the moment.”43 All this is entirely right, so long as we stress his final words—“at the moment.” An ideal theory of justice seeks to orient our long-term quest for justice, not its time/agent-indexed best feasible moves. But this is not to say that even an ideal theory is about only what we should think, not what we should do. They are not ultimately separable, for to think about justice is to think about where we should move, and how to engage in this quest (§I.1.5). Note that our similarity ordering (SO) is by no means irrelevant to a type of feasibility: we would expect a reasonably high correlation between similarity as I have characterized it and many judgments of feasibility. If a world is very close to our own, and only a few changes needed to bring it about, it is likely to be feasible, at least in the middle term, for political institutions and social movements to bring it about. I hasten to add that this will not always be the case, but it often will be, and in most cases we can readily see how we possess the “social technology” to bring the new state about—we can identify a modest number of changes that would need to be made.
2 RUGGED LANDSCAPE MODELS OF IDEAL JUSTICE
2.1 Smooth v. Rugged Optimization
An evaluative perspective, then, allows us to make judgments about the justice and structural similarity of a set of social worlds. Sometimes, as figure 2-2 indicates, a perspective can show us that our search for the ideal will be easy. Here the x-axis represents Σ’s understanding of the underlying structure of social worlds a through n in {X}, based on their similarity (as Σ sees it), as required by the Orientation Condition. The y-axis represents Σ’s evaluation of the inherent justice of these worlds satisfying the Social Realizations Condition. On this fortunate perspective, often called a “Mount Fuji” perspective, marginal changes in the underlying structure are always associated with marginal changes in their justice. As we move from social world a toward u, every small change in social structure leads to a small increase in justice. Similarly, as we move from u toward n, each small change yields a small loss in justice. Finding the ideal, u, is theoretically simple. First move from where you are. If you get to a more just social world, keep going in that direction. If and when you get to a less just social world, stop, and move back in the opposite direction: keep on moving in that direction until a marginal change yields a less just world. Finally, move one step back and you will have arrived at the ideal, the most just social world!
Figure 2-2. A Mount Fuji optimization landscape
Notice that under these conditions, even if we add the similarity and distance metrics to our model so as to satisfy the Orientation Condition, securing justice is essentially captured by Sen’s “climbing” model (§I.1.3). We really do not have to know that the highest “peak” is world u; all we need to know is which way is “up.” There really is no important sense in which the ideal orients our efforts to seek more justice. Thus we see that a model that includes the Orientation Condition does not prejudge the dispute between Sen and Simmons (§I.1.3), it merely allows us to make sense of it. We should read Simmons as maintaining that the ideal theorist seldom faces such a straightforward optimization problem.
Now it may be thought that, pace Simmons, ideal theory does, in the end, confront relatively “Mt. Fuji-ish” problems. The thought is this: as we have seen, an evaluative perspective arrays social worlds in terms of their justice-relevant properties—world a is closer to world b than to c if and only if its justice-relevant features are more similar to b than to c. Suppose that b’s justice-relevant features are very much like a’s; we would expect that the justice score of b would, then, be very much like a’s. As we move further away from a, we might expect the justice score of c would be closer to b’s than a’s, while the justice score of d, the next world out, would be closer to c’s than to b’s, and closer to b’s score than to a’s. And so on. If so, it looks like a smoot
h justice score curve up (or down) from a, perhaps peaking at world u. On the face of it, minor changes in the justice-relevant features of the social world (WF) should be closely correlated with the social world’s justice as measured by the evaluative standards (ES) and mapping function (MP).
Alas, this attractive picture is misleading. As I pointed out above, Rawls insisted that the “idea of realistic Utopia is importantly institutional,”44 and indeed the importance of institutions is a theme throughout utopian thought (§I.1.2). This is crucial: the justice of an institution, practice, or policy can be dependent on what other institutions or policies are in effect, as shown in figure 2-3. Here I consider what might be described as a “bleeding-heart libertarian perspective”—that is, a perspective on justice that places great weight on free markets and small states, but also values basic government aid to the less well-off.45 Now: