Ronald Bosman, Heike Hennig‐Schmidt, and Frans van Winden, “Emotion at Stake: The Role of Stake Size and Emotions in a Power-To-Take Game Experiment in China with a Comparison to Europe.” CESifo Working Paper Series No. 5858, April 19, 2016.
• In the two-player power-to-take game, Player A indicates what percentage of Player B’s monetary endowment Player A will claim.
• Player B learns of A’s claim, and then can choose to destroy some or all of her own endowment. Whatever is left after the destruction, Player A receives the chosen percentage of it, while Player B retains the remainder.
• Power-to-take is sort of a generalized version of the ultimatum game, and in particular, it allows Player B to have intermediate responses, in between accepting Player A’s suggestion or destroying the entire “endowment.”
• Three conditions: China Low (stakes), n=36; China High, n=36; and, EU, n=40. The results for the two China treatments are similar.
• Take rates in China average more than 50%; while most people do not destroy any of their endowment, the average amount of destruction is considerable, more than 20%. Higher take rates lead to more destruction.
• Higher take rates strengthen negative emotions in Players B, and it is possibly worse with higher stakes.
• Destruction decisions seem to be mostly driven by emotions.
• The results, including emotional responses, seem to be similar in China and Europe.
Showing posts with label ultimatum game. Show all posts
Showing posts with label ultimatum game. Show all posts
Friday, February 23, 2018
Monday, July 6, 2015
On Varying the Stakes in Ultimatum Games (2011)
Steffen Andersen, Seda Ertaç, Uri Gneezy, Moshe Hoffman, and John A.
List, “Stakes Matter in Ultimatum Games.” American Economic Review
101: 3427–3439, December 2011.
• A standard result is that varying the stakes does not lead to much of a change in the outcomes of ultimatum game (and related game) experiments. The ultimatum game is of interest in itself, but also because it seems to hold lessons for any “take-it-or-leave-it” bargaining situation.
• Andersen et al. (2011) challenge this standard result. In particular, they hope to see if “proposers” offer more “unfair” splits when the stakes are high, and if responders turn down unfair splits, even when the stakes are significant.
• In the reported experiments, conducted in villages in India, the stakes are altered by a factor of 1000. The highest-stake version is on the order of one-year’s income.
• The ultimatum game that the authors employ is structured in such a way as to nudge proposers into making “unfair” offers. Otherwise, the experimenters suspect that there will not be enough unfair offers to test reliably the willingness of responders to turn down unfair offers at high stakes. (The ultimatum game as it is typically implemented has its own share of nudge issues.)
• In the experiments, raising the stakes monotonically decreases the average percentage of the pie “offered,” though the absolute monetary amount offered increases. At the highest stakes, there is but one rejection in 24 trials. Nevertheless, at the second-highest level of stakes (about one-month's income), more than one-quarter of the proposals are rejected.
• Is it ethical to go to relatively poor villages and offer some people the potential for one year's or one month's income -- along with the (likely) prospect that some of those selected people will proceed to "lose" that stake, after being nudged towards an "unfair" offer that raises the probability of their receiving nothing? Behavioral economics experiments sometimes challenge the Kantian precept that people are to be treated as ends in themselves, not means to the ends of others.
• A standard result is that varying the stakes does not lead to much of a change in the outcomes of ultimatum game (and related game) experiments. The ultimatum game is of interest in itself, but also because it seems to hold lessons for any “take-it-or-leave-it” bargaining situation.
• Andersen et al. (2011) challenge this standard result. In particular, they hope to see if “proposers” offer more “unfair” splits when the stakes are high, and if responders turn down unfair splits, even when the stakes are significant.
• In the reported experiments, conducted in villages in India, the stakes are altered by a factor of 1000. The highest-stake version is on the order of one-year’s income.
• The ultimatum game that the authors employ is structured in such a way as to nudge proposers into making “unfair” offers. Otherwise, the experimenters suspect that there will not be enough unfair offers to test reliably the willingness of responders to turn down unfair offers at high stakes. (The ultimatum game as it is typically implemented has its own share of nudge issues.)
• In the experiments, raising the stakes monotonically decreases the average percentage of the pie “offered,” though the absolute monetary amount offered increases. At the highest stakes, there is but one rejection in 24 trials. Nevertheless, at the second-highest level of stakes (about one-month's income), more than one-quarter of the proposals are rejected.
• Is it ethical to go to relatively poor villages and offer some people the potential for one year's or one month's income -- along with the (likely) prospect that some of those selected people will proceed to "lose" that stake, after being nudged towards an "unfair" offer that raises the probability of their receiving nothing? Behavioral economics experiments sometimes challenge the Kantian precept that people are to be treated as ends in themselves, not means to the ends of others.
Levitt and List (2007) on Laboratory Experiments
Steven D. Levitt and John A. List, “What Do Laboratory Experiments Measuring Social Preferences Reveal about the Real World?” Journal of Economic Perspectives 21(2): 153-174, Spring, 2007.
• Model notation: action choice a; wealth W; stakes (or value) v; moral cost M; social norms n; and scrutiny s.
• The higher the negative financial externality an action imposes on others, the higher the moral cost M is taken to be. (Levitt and List also assume that this externality increases with the stakes v). M is higher the greater the deviation between action a and the social norm n. M also is raised by increased scrutiny s.
• Individual utility is U(a, v, n, s) = M(a, v, n, s) + W(a, v). Higher stakes v can raise W while raising M, too, but the authors assume that W rises more quickly with v. (Another interpretation might be that the norm changes with v, so that selfish behavior receives more social imprimatur.)
• Scrutiny is different and typically more intense in the lab, exaggerating pro-social behaviors. (Alternatively, scrutiny from one’s children or other family members has no lab parallel.) Further, lab participants might believe that an experiment demands some pro-social behavior.
• Behavior might be sensitive to factors that unavoidably vary between the lab and the real world: the experimenter cannot fully control the context. Participants bring context with them, and hence are playing a different game.
• Lab participants self-select, directly or indirectly, while market participants self-select, too.
Fehr and Gächter (2000) on Reciprocity
Ernst Fehr and Simon Gächter, “Fairness and Retaliation: The Economics of Reciprocity.” Journal of Economic Perspectives 14(3): 159-181, Summer, 2000.
• Positive reciprocity is when a kind act is met with kindness in return; negative reciprocity is when an unkind or unfair act is met with retaliation.
• The existence of a subset of reciprocal actors can enforce cooperative norms, though details of the environment will matter as to whether cooperation will out.
• In the Ultimatum Game, offers of less than 30% of the stake often get rejected, indicating that some types of negative reciprocity are common. But some 20 or 30 percent of folks do not reciprocate. People might be a little more likely to be negative reciprocators (punishing unfair acts) than positive reciprocators (rewarding good behavior).
• In some settings, the behavior of reciprocal people and self-interested people eventually becomes indistinguishable, whether for cooperating or free riding; that is, their motives are different, but their actual behaviors can be identical. Opportunities to punish free riders are key to sustaining cooperation.
• Reciprocity can promote contract enforcement.
• The provision of explicit incentives in a contractual relationship can engender mistrust and lead to lessened effort. As a result, firms might prefer incomplete contracts that lead to a sort of “gift exchange” and high effort.
• Positive reciprocity is when a kind act is met with kindness in return; negative reciprocity is when an unkind or unfair act is met with retaliation.
• The existence of a subset of reciprocal actors can enforce cooperative norms, though details of the environment will matter as to whether cooperation will out.
• In the Ultimatum Game, offers of less than 30% of the stake often get rejected, indicating that some types of negative reciprocity are common. But some 20 or 30 percent of folks do not reciprocate. People might be a little more likely to be negative reciprocators (punishing unfair acts) than positive reciprocators (rewarding good behavior).
• In some settings, the behavior of reciprocal people and self-interested people eventually becomes indistinguishable, whether for cooperating or free riding; that is, their motives are different, but their actual behaviors can be identical. Opportunities to punish free riders are key to sustaining cooperation.
• Reciprocity can promote contract enforcement.
• The provision of explicit incentives in a contractual relationship can engender mistrust and lead to lessened effort. As a result, firms might prefer incomplete contracts that lead to a sort of “gift exchange” and high effort.
Subscribe to:
Posts (Atom)