Wednesday, May 18, 2016

The Ellsberg Paradox and Ambiguity Aversion

• OK, this is not really an outline of an article, but at least there is an urn involved. The urn has a total of 90 balls inside of it. Thirty of the balls are black, and the other 60 balls are either red or blue. (That is, anywhere between 0 and 60 of those balls are red, and the remainder of the non-black, non-red balls are blue.) A single ball will be pulled at random from the urn. 

• Situation A: You can choose Bet 1A, which pays $100 if the ball that is chosen is black. Alternatively, you can choose Bet 2A, which pays $100 if the chosen ball is red. Which bet do you prefer? [Spoiler alert: most folks prefer Bet 1A.]

• Situation B: You can choose Bet 1B, which pays $100 if the ball that is chosen is either black or blue. Alternatively, you can choose Bet 2B, which pays $100 if the chosen ball is either red or blue. Which bet do you prefer? [Spoiler alert: most folks prefer Bet 2B.]

• The modal choices in these hypothetical urn-related decision problems, already spoiled for you, are to choose Bet 1A and Bet 2B. 

• These modal choices are inconsistent with expected utility maximization. A person who (strictly) prefers Bet 1A to Bet 2A, and is an expected utility maximizer, must believe that the probability of choosing a black ball (here, precisely one-third) exceeds the probability of choosing a red ball. A person who (strictly) prefers Bet 2B to Bet 2A, and is an EU maximizer, must believe that the probability of choosing a black ball is smaller than the probability of choosing a red ball (because the probability of winning via the blue ball is the same in either alternative, 2A or 2B).

• The disposition that (presumably) leads to these modal choices is termed ambiguity aversion. In Situation A, the subject knows precisely the probability of winning Bet 1A, but is unsure of the probability of winning Bet 2A. In Situation B, the situation is reversed, with Bet 2B being the option with the known probability (precisely 2/3) of winning.

• The modal choices, inconsistent with expected utility maximization, are an example of what has become known as the Ellsberg Paradox, after the analysis given by Daniel Ellsberg in "Risk, Ambiguity, and the Savage Axioms," Quarterly Journal of Economics 75(4): 643-669, 1961 [pdf here]; Ellsberg's version is on pages 654-655. The version in this post follows closely the presentation in the Introduction (pages 3-4) by Adam Oliver in Behavioural Public Policy, Adam Oliver, ed., Cambridge University Press, 2013.

Kocher, Lahno, and Trautmann (2015) on Ambiguity Aversion as Exceptional

Martin G. Kocher, Amrei Marie Lahno, and Stefan T. Trautmann, “Ambiguity Aversion is the Exception.” CESifo Working Paper No. 5261, March, 2015 [pdf available for download here].

• In light of the Ellsberg paradox, many researchers believe that people are averse to ambiguity: people will accept known risks over ambiguous ones even when such choices are rather costly. 

• But ambiguity aversion has only been robustly demonstrated for choices involving uncertain gains, and where the probability of achieving a gain is moderate. What about quite unlikely risks, or prospects involving losses, or mixed (gains and losses) gambles? 

• Kocher et al. conduct a laboratory experiment with more than 500 participants. The main result is that for moderate likelihood gain prospects, ambiguity aversion exists (consistent with the prior literature). But outside of the moderate likelihood gain domain, aversion is harder to find, and even ambiguity seeking sometimes is common. Further, there’s a good deal of ambiguity neutrality in all conditions. 

• Previous work predicts: ambiguity aversion for moderately likely gains and for low likelihood losses; ambiguity seeking for low likelihood gains and moderately likely losses. Kocher et al. hypothesize ambiguity aversion for mixed gain/loss prospects. 

• In the first stage of the experiment, it is only in the moderate likelihood gain domain that ambiguity aversion (still, in a minority of participants) is indicated. Most people (in all domains) are ambiguity neutral. Once the effectively neutral folks are purged from the data set, what remains is the predicted pattern: ambiguity aversion in the gains domain for moderate likelihoods, and in the losses domain for low likelihoods; ambiguity seeking for low-likelihood gains and moderate likelihood losses. 

• For mixed gain/loss prospects, once again ambiguity neutrality dominates. When the neutrals are removed from the data set in this domain, the only statistically significant finding is ambiguity seeking for a .1 (average) chance to win 10 euro paired with a .9 (average) chance to lose 10 euro.

Thursday, May 5, 2016

Bhargava and Loewenstein (2015) Want to Go Beyond Nudging

Saurabh Bhargava and George Loewenstein, “Behavioral Economics and Public Policy 102: Beyond Nudging.” American Economic Review 105(5): 396-401, 2015.

• The early behavioral economics-influenced policy proposals were aimed at internalities, and at nudging decisions quite proximate to the perceived problem. 

• The next stage, Bhargava and Loewenstein argue, should be to influence the design of policies that are more fundamental, but perhaps less proximate, to perceived problems. This approach need not be particularly controversial, given that the targeted problems often implicate externalities or other market failures. 

• Successful nudges have included easing the way to save more for retirement, along with improving the disclosure of information so that people receive useful information in a manner that is easy to understand and respond to. 

• Proposed principle #1: not only should choice environments be simplified, the objects of choice should be simplified. Financial products, for instance, could be required to be simple and to be presented in a standardized form. 

• Proposed principle #2: policy should look to counter nudges by the private sector that are detrimental to consumers. 

• Proposed principle # 3: traditional policy instruments, such as taxes, should be modified in a manner informed by behavioral considerations (framing, salience, inattention, etc.) to maximize the policies’ impacts. 

• Examples that the authors discuss include health insurance (lots of room for simplification and standardization); privacy and disclosure (again, simplification and standardization, along with controls on misleading disclosures); and climate change (contending against the many psychological dispositions that make it hard to recognize or respond to this global public bad).