How individual preferences are aggregated in groups: An experimental study

Ambrus, Attila; Greiner, Ben; Pathak, Parag A.

doi:10.1016/j.jpubeco.2015.05.008

Cited by 42 publications

(23 citation statements)

References 84 publications

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“…The main difference between our hypothesis and that of Ambrus et al [6] is that we assume heterogeneous individual discount factors, whereas they assume common discount factors among members of a group. Our assumption is closer to how group decisions are made in real-life.…”

Section: Multilateral Bargaining Hypothesismentioning

confidence: 76%

“…Our experiment differs from these bargaining models, where members decide how to allocate the resource among themselves, because members decide on a common group discount factor (i.e., time preferences) in our experimental setting. Here, we follow Ambrus et al [6], who studied how individual risk preferences were aggregated in groups and also employed the multilateral bargaining model as a theoretical background. We do so because we also investigate how individual preferences are aggregated in groups.…”

Section: Multilateral Bargaining Hypothesismentioning

confidence: 99%

“…We classify group members into three ranks, as follows. Rank 1, rank 2, and rank 3 represent the order of the discount factor under the individual condition in the same group 6 . Rank 1 refers to the most patient member (the highest discount factor) in a group, rank 2 refers to the median member (the second highest discount factor), and rank 3 refers to the most impatient member (the third highest discount factor: i.e., the lowest discount factor).…”

Section: Regression Analysis and Post-estimation Analysismentioning

confidence: 99%

“…For a more detailed analysis, we conduct a regression analysis and a post-estimation analysis. These analyses are partially based on the work of Ambrus et al [6], who also analyze how individual preferences are aggregated at the group level. They investigated risk preferences using a lottery task and selfishness using a gift exchange game.…”

Section: Regression Analysis and Post-estimation Analysismentioning

confidence: 99%

“…He and Villeval [5] show that group decisions reflect the same level of inequality aversion as individual decisions. Many studies investigated the difference between group and individual risk preferences [6][7][8][9][10][11][12]. Although an increasing number of studies are comparing groups' and individuals' decision-making, few studies focus on groups' intertemporal decision-making [2,3].…”

Section: Introductionmentioning

confidence: 99%

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How Are Individual Time Preferences Aggregated in Groups? A Laboratory Experiment on Intertemporal Group Decision-Making

Tsuruta

Inukai

2018

Front. Appl. Math. Stat.

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The study of intertemporal decision-making is an interdisciplinary scientific topic of economics, psychology, and neuroscience. Most of these studies focus on individual intertemporal decisions, but little is known about the relationship between groups and individual time preferences. As a result, we intend to assess the role of group intertemporal decision-making. We experimentally investigate how to aggregate individual time preferences by clarifying who has the most influence on group decisions among heterogeneous group members. We formulate two hypotheses. The first is the multilateral bargaining hypothesis, which is based on the multilateral bargaining model. If people employ this model to reach agreement, the most patient member in a group has the greatest impact on group choices. The second is the median voter hypothesis, which is based on the median voter model. When people employ this model to reach agreement, the median patient member in a group has the greatest impact on group choices. Here, we find that the median patient member in a group has a significant impact on group decisions in an unstructured bargaining situation. This finding suggests that people use the majority voting rule during group intertemporal decision-making. Thus, our findings support the median voter hypothesis. Furthermore, the results of a chat analysis show that this result is partially due to people's conformity with the majority opinion.

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Section: Multilateral Bargaining Hypothesismentioning

confidence: 76%

Section: Multilateral Bargaining Hypothesismentioning

confidence: 99%

Section: Regression Analysis and Post-estimation Analysismentioning

confidence: 99%

Section: Regression Analysis and Post-estimation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Are Individual Time Preferences Aggregated in Groups? A Laboratory Experiment on Intertemporal Group Decision-Making

Tsuruta

Inukai

2018

Front. Appl. Math. Stat.

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Public Deliberation about Gene Editing in the Wild

Gusmano¹,

Kaebnick²,

Maschke³

et al. 2021

Hastings Center Report

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Genetic editing technologies have long been used to modify domesticated nonhuman animals and plants. Recently, attention and funding have also been directed toward projects for modifying nonhuman organisms in the shared environment—that is, in the “wild.” Interest in gene editing nonhuman organisms for wild release is motivated by a variety of goals, and such releases hold the possibility of significant, potentially transformative benefit. The technologies also pose risks and are often surrounded by a high uncertainty. Given the stakes, scientists and advisory bodies have called for public engagement in the science, ethics, and governance of gene editing research in nonhuman organisms. Most calls for public engagement lack details about how to design a broad public deliberation, including questions about participation, how to structure the conversations, how to report on the content, and how to link the deliberations to policy. We summarize the key design elements that can improve broad public deliberations about gene editing in the wild.

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