How collective reward structure impedes group decision making: An experimental study using the HoneyComb paradigm

This study investigates if and under which conditions humans are able to identify and follow the most advantageous leader who will them provide with the most resources. In an iterated economic game with the aim of earning monetary reward, 150 participants were asked to repeatedly choose one out of four leaders. Unbeknownst to participants, the leaders were computer-controlled and programmed to yield different expected payout values that participants had to infer from repeated interaction over 30 rounds. Additionally, participants were randomly assigned to one of three conditions: single, independent, or cohesion. The conditions were designed to investigate the ideal circumstances that lead to identifying the most advantageous leader: when participants are alone (single condition), in a group that lets individuals sample information about leaders independently (independent condition), or in a group that is rewarded for cohesive behavior (cohesion condition). Our results show that participants are generally able to identify the most advantageous leader. However, participants who were incentivized to act cohesively in a group were more likely to settle on a less advantageous leader. This suggests that cohesion might have a detrimental effect on group decision making. To test the validity of this finding, we explore possible explanations for this pattern, such as the length of exploration and exploitation phases, and present techniques to check for confounding factors in group experiments in order to identify or exclude them as alternative explanations. Finally, we show that the chosen reward structure of the game strongly affects the observed following behavior in the group and possibly occludes other effects. We conclude with a recommendation to carefully choose reward structures and evaluate possible alternative explanations in experimental group research that should further pursue the study of exploration/exploitation phases and the influence of group cohesion on group decision making as promising topics for further research.

Selection

Firms receive profits and losses, while individuals and initiatives generally only receive awards as mediated by an organization’s accounting system and reward structure. In that sense, a firm can be considered to be a credit assignment mechanism. Three basic challenges are considered: the problem of diversity of selection criteria, the challenge of the timing of selection relative to developmental processes, and the issue of units of aggregation and selection. Selection inevitably must be made on the basis of various imperfect indicators of broader objectives. The diversity of these selection criteria is argued to be an under-appreciated facet of diversity as prior work has tended to focus attention on the degree of diversity of underlying initiatives and activities. Further, it is recognized that the environment, or contexts, in which the organization operates, is itself an object of selection, which in turn influences the feedback processes the organization experiences.

Cooperative, Competitive and Hybrid Motivated Information Processing in Teams

<p></p><p>Currently, there is a proliferation of studies examining group and team dynamics, as an increasing number of organizations are incorporating group and team-based structures. Extant literature has provided mixed findings regarding the influence of reward structures on various team outcomes. E.g., More rapid activity in teams has been found for competitive reward structure, with diminishing quality of work (Mailer, 1929). Which type of reward structure (competitive, cooperative or hybrid) has the best implication for team performance in organizations? This paper aims to address this question by incorporating the mechanism (i.e., epistemic and social motivation) and moderating variable (i.e., need for affiliation). Understanding the motivated information processing in groups model (MIP-G) and antecedents of team performance has important managerial implications. This moderator (i.e., need for affiliation) has not been examined in relation to all three types of reward structures in past studies. This paper hopes to extend the literature of reward structures and motivated information processing model by encompassing this new boundary condition. It is essential to incorporate novel contexts while examining relationships among variables as this approach further develops existing theories.</p><br><p></p>

Cooperative, Competitive and Hybrid Motivated Information Processing in Teams

<p></p><p>Currently, there is a proliferation of studies examining group and team dynamics, as an increasing number of organizations are incorporating group and team-based structures. Extant literature has provided mixed findings regarding the influence of reward structures on various team outcomes. E.g., More rapid activity in teams has been found for competitive reward structure, with diminishing quality of work (Mailer, 1929). Which type of reward structure (competitive, cooperative or hybrid) has the best implication for team performance in organizations? This paper aims to address this question by incorporating the mechanism (i.e., epistemic and social motivation) and moderating variable (i.e., need for affiliation). Understanding the motivated information processing in groups model (MIP-G) and antecedents of team performance has important managerial implications. This moderator (i.e., need for affiliation) has not been examined in relation to all three types of reward structures in past studies. This paper hopes to extend the literature of reward structures and motivated information processing model by encompassing this new boundary condition. It is essential to incorporate novel contexts while examining relationships among variables as this approach further develops existing theories.</p><br><p></p>

Dynamic Representation of the Subjective Value of Information

To improve future decisions, people should seek information based on the value of information (VOI), which depends on the current evidence and the reward structure of the upcoming decision. When additional evidence is supplied, people should update VOI to adjust subsequent information seeking, but the neurocognitive mechanisms of this updating process remain unknown. We used a modified beads task to examine how the VOI is represented and updated in the human brain. We theoretically derived, and empirically verified, a normative prediction that the VOI depends on decision evidence and is biased by reward asymmetry. Using fMRI, we found that the subjective VOI is represented in right dorsolateral prefrontal cortex (DLPFC). Critically, this VOI representation was updated when additional evidence was supplied, showing that DLPFC dynamically tracks the up-to-date VOI over time. These results provide new insights into how humans adaptively seek information in the service of decision making.

Leonardo da Vinci, pre-registration and the architecture of science: Towards a more open and transparent research culture

There has been much talk of psychological science undergoing a renaissance with recent years being marked by dramatic changes in research practices and to the publishing landscape. This article briefly summarises a number of the ways in which psychological science can improve its rigor, lessen use of questionable research practices and reduce publication bias. The importance of pre-registration as a useful tool to increase transparency of science and improve the robustness of our evidence base, especially in COVID-19 times, is presented. In particular, the case for the increased adoption of Registered Reports, the article format that allows peer review of research studies before the results are known, is outlined. Finally, the article argues that the scientific architecture and the academic reward structure need to change with a move towards “slow science” and away from the “publish or perish” culture.

The roles of online and offline replay in planning

Animals and humans replay neural patterns encoding trajectories through their environment, both whilst they solve decision-making tasks and during rest. Both on-task and off-task replay are believed to contribute to flexible decision making, though how their relative contributions differ remains unclear. We investigated this question by using magnetoencephalography (MEG) to study human subjects while they performed a decision-making task that was designed to reveal the decision algorithms employed. We characterised subjects in terms of how flexibly each adjusted their choices to changes in temporal, spatial and reward structure. The more flexible a subject, the more they replayed trajectories during task performance, and this replay was coupled with re-planning of the encoded trajectories. The less flexible a subject, the more they replayed previously preferred trajectories during rest periods between task epochs. The data suggest that online and offline replay both participate in planning but support distinct decision strategies.

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets.

The roles of online and offline replay in planning

Animals and humans replay neural patterns encoding trajectories through their environment, both whilst they solve decision-making tasks and during rest. Both on-task and off-task replay are believed to contribute to flexible decision making, though how their relative contributions differ remains unclear. We investigated this question by using magnetoencephalography to study human subjects while they performed a decision-making task that was designed to reveal the decision algorithms employed. We characterized subjects in terms of how flexibly each adjusted their choices to changes in temporal, spatial and reward structure. The more flexible a subject, the more they replayed trajectories during task performance, and this replay was coupled with re-planning of the encoded trajectories. The less flexible a subject, the more they replayed previously and subsequently preferred trajectories during rest periods between task epochs. The data suggest that online and offline replay both participate in planning but support distinct decision strategies.