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Human collective intelligence under dual exploration-exploitation dilemmas.

ABSTRACT: The exploration-exploitation dilemma is a recurrent adaptive problem for humans as well as non-human animals. Given a fixed time/energy budget, every individual faces a fundamental trade-off between exploring for better resources and exploiting known resources to optimize overall performance under uncertainty. Colonies of eusocial insects are known to solve this dilemma successfully via evolved coordination mechanisms that function at the collective level. For humans and other non-eusocial species, however, this dilemma operates within individuals as well as between individuals, because group members may be motivated to take excessive advantage of others' exploratory findings through social learning. Thus, even though social learning can reduce collective exploration costs, the emergence of disproportionate "information scroungers" may severely undermine its potential benefits. We investigated experimentally whether social learning opportunities might improve the performance of human participants working on a "multi-armed bandit" problem in groups, where they could learn about each other's past choice behaviors. Results showed that, even though information scroungers emerged frequently in groups, social learning opportunities reduced total group exploration time while increasing harvesting from better options, and consequentially improved collective performance. Surprisingly, enriching social information by allowing participants to observe others' evaluations of chosen options (e.g., Amazon's 5-star rating system) in addition to choice-frequency information had a detrimental impact on performance compared to the simpler situation with only the choice-frequency information. These results indicate that humans groups can handle the fundamental "dual exploration-exploitation dilemmas" successfully, and that social learning about simple choice-frequencies can help produce collective intelligence.

SUBMITTER: Toyokawa W

PROVIDER: S-EPMC3995913 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Innovative ways of planning and conducting research have emerged recently, based on the concept of collective intelligence. Collective intelligence is defined as shared intelligence emerging when people are mobilized within or outside an organization to work on a specific task that could result in more innovative outcomes than those when individuals work alone. Crowdsourcing is defined as "the act of taking a job traditionally performed by a designated agent and outsourcing it to an undefined, generally large group of people in the form of an open call." OBJECTIVE:This qualitative study aimed to identify the barriers to mobilizing collective intelligence and ways to overcome these barriers and provide good practice advice for planning and conducting collective intelligence projects across different research disciplines. METHODS:We conducted a multinational online open-ended question survey and semistructured audio-recorded interviews with a purposive sample of researchers who had experience in running collective intelligence projects. The questionnaires had an interactive component, enabling respondents to rate and comment on the advice of their fellow respondents. Data were analyzed thematically, drawing on the framework method. RESULTS:A total of 82 respondents from various research fields participated in the survey (n=65) or interview (n=17). The main barriers identified were the lack of evidence-based guidelines for implementing collective intelligence, complexity in recruiting and engaging the community, and difficulties in disseminating the results of collective intelligence projects. We drew on respondents' experience to provide tips and good practice advice for governance, planning, and conducting collective intelligence projects. Respondents particularly suggested establishing a diverse coordination team to plan and manage collective intelligence projects and setting up common rules of governance for participants in projects. In project planning, respondents provided advice on identifying research problems that could be answered by collective intelligence and identifying communities of participants. They shared tips on preparing the task and interface and organizing communication activities to recruit and engage participants. CONCLUSIONS:Mobilizing collective intelligence through crowdsourcing is an innovative method to increase research efficiency, although there are several barriers to its implementation. We present good practice advice from researchers with experience of collective intelligence across different disciplines to overcome barriers to mobilizing collective intelligence.

Project description:Empirical findings on public goods dilemmas indicate an unresolved dilemma: that increasing size-the number of people in the dilemma-sometimes increases, decreases, or does not influence cooperation. We clarify this dilemma by first classifying public goods dilemma properties that specify individual outcomes as individual properties (e.g., Marginal Per Capita Return) and group outcomes as group properties (e.g., public good multiplier), mathematically showing how only one set of properties can remain constant as the dilemma size increases. Underpinning decision-making regarding individual and group properties, we propose that individuals are motivated by both individual and group preferences based on a theory of collective rationality. We use Van Lange's integrated model of social value orientations to operationalize these preferences as an amalgamation of outcomes for self, outcomes for others, and equality of outcomes. Based on this model, we then predict how the public good's benefit and size, combined with controlling individual versus group properties, produce different levels of cooperation in public goods dilemmas. A two (low vs. high benefit) by three (2-person baseline vs. 5-person holding constant individual properties vs. 5-person holding constant group properties) factorial experiment (group n = 99; participant n = 390) confirms our hypotheses. The results indicate that when holding constant group properties, size decreases cooperation. Yet when holding constant individual properties, size increases cooperation when benefit is low and does not affect cooperation when benefit is high. Using agent-based simulations of individual and group preferences vis-à-vis the integrative model, we fit a weighted simulation model to the empirical data. This fitted model is sufficient to reproduce the empirical results, but only when both individual (self-interest) and group (other-interest and equality) preference are included. Our research contributes to understanding how people's motivations and behaviors within public goods dilemmas interact with the properties of the dilemma to lead to collective outcomes.

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Human collective intelligence under dual exploration-exploitation dilemmas.

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