Regret (decision theory)

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Regret is the negative emotion experienced when learning that an alternative course of action would have resulted in a more favorable outcome. The theory of regret aversion or anticipated regret proposes that when facing a decision, individuals may anticipate the possibility of feeling regret after the uncertainty is resolved and thus incorporate in their choice their desire to eliminate or reduce this possibility.

Regret theory[edit]

Regret theory models choice under uncertainty taking into account the effect of anticipated regret. It was originally developed simultaneously by Graham Loomes and Robert Sugden,[1] David E. Bell[2] and Peter C. Fishburn[3] and subsequently improved upon by several other authors.

In general, these models incorporate a regret term to the utility function that depends negatively on the realized outcome and positively on the best alternative outcome given the uncertainty resolution. This regret term is usually an increasing, continuous and non-negative function subtracted to the traditional utility index. These type of preferences always violate transitivity[4] in the traditional sense although most satisfy a weaker version

Evidence[edit]

Several experiments over both incentivized and hypothetical choices attest to the magnitude of this effect.

Experiments in first price auctions show that by manipulating the feedback the participants expect to receive, significant differences in the average bids are observed.[5] In particular, "Loser's regret" can be induced by revealing the winning bid to all participants in the auction, and thus revealing to the loser's whether they would have been able to make a profit and how much could it have been (a participant that has a valuation of $50, bids $30 and finds out the winning bid was $35 will also learn that she could have earned as much as $15 by bidding anything over $35.) This in turn allows for the possibility of regret and if bidders correctly anticipate this, they would tend to bid higher than in the case where no feedback on the winning bid is provided in order to decrease the possibility of regret.

In decisions over lotteries, experiments also provide supporting evidence of anticipated regret.[6][7] As in the case of first price auctions, differences in feedback over the resolution of the uncertainty can cause the possibility of regret and if this is anticipated, it may induce different preferences. For example, when faced with a choice between $40 with certainty and a coin toss that pays $100 if the outcome is guessed correctly and $0 otherwise, not only does the certain payment alternative minimizes the risk but also the possibility of regret, since typically the coin will not be tossed (and thus the uncertainty not resolved) while if the coin toss is chosen, the outcome that pays $0 will induce regret. If the coin is tossed regardless of the chosen alternative, then the alternative payoff is will always be known and then there is no choice that will eliminate the possibility of regret.

Applications[edit]

Besides the traditional setting of choices over lotteries, regret aversion has been proposed as an explanation for the typically observed overbidding in first price auctions[8] and the disposition effect[9] among others.

Minimax regret[edit]

The minimax regret approach is to minimize the worst-case regret.[10] The aim of this is to perform as closely as possible to the optimal course. Since the minimax criterion applied here is to the regret (difference or ratio of the payoffs) rather than to the payoff itself, it is not as pessimistic as the ordinary minimax approach. Similar approaches have been used in a variety of areas such as:

One benefit of minimax (as opposed to expected regret) is that it is independent of the probabilities of the various outcomes: thus if regret can be accurately computed, one can reliably use minimax regret. However, probabilities of outcomes are hard to estimate.

This differs from the standard minimax approach in that it uses differences or ratios between outcomes, and thus requires interval or ratio measurements, as well as ordinal measurements (ranking), as in standard minimax.

Maximin example[edit]

Suppose an investor has to choose between investing in stocks, bonds or the money market, and the total return depends on what happens to interest rates. The following table shows some possible returns:

Return Interest rates rise Static rates Interest rates fall Worst return
Stocks −4 4 12 −4
Bonds −2 3 8 −2
Money market 3 2 1 1
Best return 3 4 12

The crude maximin choice based on returns would be to invest in the money market, ensuring a return of at least 1. However, if interest rates fell then the regret associated with this choice would be large. This would be −11, which is the difference between the 1 received and the 12 which could have been received if the outturn had been known in advance. A mixed portfolio of about 11.1% in stocks and 88.9% in the money market would have ensured a return of at least 2.22; but, if interest rates fell, there would be a regret of about −9.78.

The regret table for this example, constructed by subtracting best returns from actual returns, is as follows:

Regret Interest rates rise Static rates Interest rates fall Worst regret
Stocks −7 0 0 −7
Bonds −5 −1 −4 −5
Money market 0 −2 −11 −11

Therefore, using a minimax choice based on regret, the best course would be to invest in bonds, ensuring a regret of no worse than −5. A mixed investment portfolio would do even better: 61.1% invested in stocks, and 38.9% in the money market would produce a regret no worse than about −4.28.

Example: Linear estimation setting[edit]

What follows is an illustration of how the concept of regret can be used to design a linear estimator. The regret is the difference between the mean-squared error (MSE) of the linear estimator that doesn't know the parameter x, and the mean-squared error (MSE) of the linear estimator that knows x. Also, since the estimator is restricted to be linear, the zero MSE cannot be achieved in the latter case.

Consider the problem of estimating the unknown deterministic parameter vector x from the noisy measurements in the linear model

y=Hx+w

where H is a known n \times m matrix with full column rank m, and w is a zero mean random vector with covariance matrix C_w, which models the noise.

Let

\hat{x}=Gy

be a linear estimate of x from y, where G is some m \times n matrix. The MSE of this estimator is given by

MSE = E\left(||\hat{x}-x||^2\right) = Tr(GC_wG^*) + x^*(I-GH)^*(I-GH)x.

Since the MSE depends explicitly on x it cannot be minimized directly. Instead, the concept of regret can be used in order to define a linear estimator with good MSE performance. To define the regret here, consider a linear estimator that knows the value of the parameter x, i.e. the matrix G can explicitly depend on x:

\hat{x}^o=G(x)y.

The MSE of \hat{x}^o is

MSE^o=E\left(||\hat{x}^o-x||^2\right) = Tr(G(x)C_wG(x)^*) + x^*(I-G(x)H)^*(I-G(x)H)x.

To find the optimal G(x), it is differentated with respect to G and equated to 0 getting

G(x)=xx^*H^*(C_w+Hxx^*H^*)^{-1}

and using the Matrix Inversion Lemma

G(x)=\frac{1}{1+x^*H^*C_w^{-1}Hx}xx^*H^*C_w^{-1}.

Substituting this G(x) back into MSE^o

MSE^o=\frac{x^*x}{1+x^*H^*C_w^{-1}Hx}.

This is the smallest MSE achievable with a linear estimate that knows x. In practice this MSE cannot be achieved, but it serves as a bound on the optimal MSE. The regret is defined by

R(x,G)=MSE-MSE^o=Tr(GC_wG^*) + x^*(I-GH)^*(I-GH)x-\frac{x^*x}{1+x^*H^*C_w^{-1}Hx}.

The minimax regret approach here is to minimize the worst-case regret as defined above. This will allow a performance as close as possible to the best achievable performance in the worst case of the parameter x. Although this problem appears difficult, it can be formulated as a convex optimization problem and solved definitely. For details of this see Eldar, Tal and Nemirovski (2004).[11] Similar ideas can be used when x is random with uncertainty in the covariance matrix. For this see Eldar and Merhav (2004), and Eldar and Merhav (2005).[12][13]

See also[edit]

References[edit]

  1. ^ Loomes, G. and Sugden, R. (1982), "Regret theory: An alternative theory of rational choice under uncertainty", Economic Journal, 92(4), 805–24.
  2. ^ Bell, D. E. (1982). Regret in decision making under uncertainty. Operations research, 30(5), 961-981.
  3. ^ Fishburn, P. C. (1982). The foundations of expected utility. Theory & Decision Library.
  4. ^ Bikhchandani, S., & Segal, U. (2011). Transitive regret. Theoretical Economics, 6(1), 95-108.
  5. ^ Filiz-Ozbay, E., & Ozbay, E. Y. (2007). Auctions with anticipated regret: Theory and experiment. The American Economic Review, 1407-1418.
  6. ^ Zeelenberg, M., Beattie, J., Van der Pligt, J., & de Vries, N. K. (1996). Consequences of regret aversion: Effects of expected feedback on risky decision making. Organizational behavior and human decision processes, 65(2), 148-158.
  7. ^ Zeelenberg, M., & Beattie, J. (1997). Consequences of regret aversion 2: Additional evidence for effects of feedback on decision making. Organizational Behavior and Human Decision Processes, 72(1), 63-78.
  8. ^ Engelbrecht-Wiggans, R. (1989). The effect of regret on optimal bidding in auctions. Management Science, 35(6), 685-692.
  9. ^ Fogel, S. O. C., & Berry, T. (2006). The disposition effect and individual investor decisions: the roles of regret and counterfactual alternatives. The Journal of Behavioral Finance, 7(2), 107-116.
  10. ^ Savage, L.J. (I95I). "The theory of statistical decision." Journal of the American Statistical Association, vol. 46, pp. 55–67.
  11. ^ Y. C. Eldar, A. Ben-Tal, and A. Nemirovski, "Linear Minimax regret estimation of deterministic parameters with bounded data uncertainties," IEEE Trans. Signal Process., vol. 52, no. 8, pp. 2177–2188, Aug. 2004.
  12. ^ Y. C. Eldar and Neri Merhav, "A Competitive Minimax Approach to Robust Estimation of Random Parameters," IEEE Trans. Signal Processing, vol. 52, pp. 1931–1946, July 2004.
  13. ^ Y. C. Eldar and Neri Merhav, "Minimax MSE-Ratio Estimation with Signal Covariance Uncertainties," IEEE Trans. Signal Processing, vol. 53, no. 4, pp. 1335–1347, Apr. 2005.

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