Pareto optimality
Dana R. Clyman and Thomas M. Tripp
A Pareto optimal allocation is one from which it is impossible to improve any party’s share with out diminishing the share of another party.
When allocating resources, whether through a negotiation process, political process, or simply by edict, the question arises: How do you know whether the final allocation is a ‘‘good’’ one? Regardless of one’s philosophy, a necessary condition for ‘‘goodness’’ with which few people would argue is Pareto optimality. One would never want to accept a non Pareto optimal allocation, because such an allocation could always be improved upon for at least one party – if not for all parties – without requiring any sacrifice from any other party.
This idea is presented graphically in figure 1, which depicts the collection of possible resolutions of a two party allocation decision, as measured by the value each party derives from each possible resolution. The points on and within the curve represent the values to the parties of the possible allocations. Because no allocations are mapped to points outside the curved boundary, that boundary represents the
Figure 1
collection of Pareto optimal allocations. No allocation represented by a point within the curved space is Pareto optimal because there is always another allocation on the curved boundary that is preferred by both parties.
For instance, compare the allocations whose values to the parties are depicted by points A and B. No matter what the value systems are to which the individuals subscribe, as long as the individual’s values are measured accurately, then both parties must prefer allocation B to allocation A. It follows immediately, therefore, that all ‘‘good’’ allocations are Pareto optimal.
But are all Pareto optimal allocations good? Consider allocation C. This allocation could be described as ‘‘Y gets everything; X gets nothing.’’ In a multiparty allocation decision, equivalent allocations are those where one party or a few parties get everything, and everyone else gets nothing. Few would welcome such out comes. The reason is clear: Pareto optimality says nothing about fairness. In other words, while few would disagree with the idea that Pareto optimality is a necessary condition for assessing the ‘‘goodness’’ of allocations, few would argue that it is sufficient (see fairness).
There is also an additional problem with Pareto optimality. When one moves from few parties to many, the concept itself becomes less useful. The reason is that, as the number of parties increases, the probability increases dramatically that at least one party would be made worse off whenever one allocation is replaced by another. In other words, in multiparty allocation decisions, a far greater percentage of the collection of possible allocations are Pareto optimal. And, in the extreme, when all allocations are Pareto optimal, Pareto optimality cannot dis criminate among allocations. Hence, as the number of parties increases, the Pareto optimality condition loses its power.
Nevertheless, the condition is still necessary. One should never accept an allocation – no matter how many parties – that is not Pareto optimal. Furthermore, in two party allocation decisions, like one on one negotiations, Pareto optimality is a powerful tool for discriminating among alternatives.
Bibliography
Clyman, D. R. (1995). Measures of joint performance in dyadic mixed-motive negotiations. Organizational Behavior and Human Decision Processes, 64, 38 48.
Clyman, D. R. (1996). Measuring cooperation in negotiations: The impossible dream. In R. J. Zeckhauser et al. (eds.), Wise Choices: Decisions, Games and Negotiations. Boston, MA: Harvard Business School Press, 388 99.
Deutsch, M. (1975). Distributive Justice. New Haven, CT: Yale University Press.
Lax, D. A. and Sebenius, J. K. (1987). Measuring the degree of joint gains achieved by negotiators. Unpublished manuscript, Harvard University.
Lind, E. A. and Tyler, T. R. (1988). The Social Psychology of Procedural Justice. New York: Plenum Press.
Raiffa, H. (1982). The Art and Science of Negotiation. Cambridge, MA: Harvard University Press.
Tripp, T. M. and Sondak, H. (1992). An evaluation of dependent variables in experimental negotiation studies: Impasse rates and Pareto efficiency. Organizational Behavior and Human Decision Processes, 51, 273 95.