Naïve individuals promote collective exploration in homing pigeons

Group-living animals that rely on stable foraging or migratory routes can develop behavioural traditions to pass route information down to inexperienced individuals. Striking a balance between exploitation of social information and exploration for better alternatives is essential to prevent the spread of maladaptive traditions. We investigated this balance during cumulative route development in the homing pigeon Columba livia. We quantified information transfer within pairs of birds in a transmission-chain experiment and determined how birds with different levels of experience contributed to the exploration–exploitation trade-off. Newly introduced naïve individuals were initially more likely to initiate exploration than experienced birds, but the pair soon settled into a pattern of alternating leadership with both birds contributing equally. Experimental pairs showed an oscillating pattern of exploration over generations that might facilitate the discovery of more efficient routes. Our results introduce a new perspective on the roles of leadership and information pooling in the context of collective learning.

Soft and hard information in equity crowdfunding: network effects in the digitalization of entrepreneurial finance

As a digital financial innovation, equity crowdfunding (ECF) allows investors to exploit the complementarity of information provision and network effects in a reduced transaction cost environment. We build on the underlying distinction between soft and hard information and show that ECF platforms create an environment of greater information pooling that benefits from network externalities. We test our hypotheses using a unique proprietary dataset and find that soft information has a greater impact than hard on the likelihood that a financing pitch will be successful. Moreover, the effects of soft information are amplified by the size of the investor network on the platform and network size also positively moderates the effect of information on the amount invested during each pitch. We conclude that ECF platforms can successfully exploit low transaction costs of the digital environment and bring network externalities to bear on investor decisions. Taken together that these increase the supply of funds to entrepreneurs.

Naïve individuals promote collective exploration in homing pigeons

Group-living animals that rely on stable foraging or migratory routes can develop behavioural traditions to pass route information down to inexperienced individuals. Striking a balance between exploitation of social information and exploration for better alternatives is essential to prevent the spread of maladaptive traditions. We investigated this balance during cumulative route development in the homing pigeon Columba livia. We quantified causal interactions within pairs of birds in a transmission-chain experiment and determined how birds with different levels of experience contributed to the exploration–exploitation trade-off. Newly introduced naïve individuals were initially more likely to initiate exploration than experienced birds, but the pair soon settled into a pattern of alternating leadership with both birds contributing equally. Experimental pairs showed an oscillating pattern of exploration over generations that might facilitate the discovery of more efficient routes. Our results introduce a new perspective on the roles of leadership and information pooling in the context of collective learning.

Modeling Information Pooling Bias in Incident Response Teams: An Agent Based Modeling Approach

Security analysts regularly correlate disparate incidents to detect cyber-attacks. However, past research shows that team-based incident correlation analysis may be affected by information pooling bias. This article presents findings from an agent-based model used to explore the cognitive processes hypothesized to be causing this bias during information exchange within a team. The model simulated information exchange between three analysts conducting incident correlation analysis by searching for information available with them about the different incidents. Three models of memory search process were compared: Random, Local, and Memory-aided search. Results from the simulation show that agents in a local search model, compared to memory-aided search model, shared more often the information known to majority in the team. Comparing model results with data from lab experiments suggest that teams, by default, may be employing a heuristic search process during information exchange leading to sub-optimal team processes and performance.

Group size and composition influence collective movement in a highly social terrestrial bird

A challenge of group-living is to maintain cohesion while navigating through heterogeneous landscapes. Larger groups benefit from information pooling, translating to greater ‘collective intelligence’, but face increased coordination challenges. If these facets interact, we should observe a non-linear relationship between group size and collective movement. We deployed high-resolution GPS tags to vulturine guineafowl from 21 distinct social groups and used continuous-time movement models to characterize group movements across five seasons. Our data revealed a quadratic relationship between group size and movement characteristics, with intermediate-sized groups exhibiting the largest home-range size and greater variation in space use. Intermediate-sized groups also had higher reproductive success, but having more young in the group reduced home-range size. Our study suggests the presence of an optimal group size, and composition, for collective movement.

How to Manage Crowdsourcing Platforms Effectively

Crowdsourced tasks are very diverse – and so are platform types. They fall into four categories, each demanding different governance mechanisms. The main goal of microtasking crowdsourcing platforms is the scalable and time-efficient batch processing of highly repetitive tasks. Crowdsourcing platforms for information pooling aggregate contributions such as votes, opinions, assessments and forecasts through approaches such as averaging, summation, or visualization. Broadcast search platforms collect contributions to solve tasks in order to gain alternative insights and solutions from people outside the organization, and are particularly suited for solving challenging technical, analytical, scientific, or creative problems. Open collaboration platforms invite contributors to team up to jointly solve complex problems in cases where solutions require the integration of distributed knowledge and the skills of many contributors. Companies establishing crowdsourcing platforms of any type should continuously monitor and adjust their governance mechanisms. Quality and quantity of contributions, project runtime, or the effort for conducting the crowdsourcing project may be good starting points.

Epistemic network injustice

To find out what is in one’s own best interest, it is helpful to ask one’s epistemic peers. However, identifying one’s epistemic peers is not a trivial task. I consider a stylized political setting, an electoral competition of ‘Masses’ and ‘Elites’. To succeed, the Masses need to know which alternative on offer is truly in their interest. To find out, the Masses can pool their privately held information in a pre-election ballot, provided that they can reliably find out with whom they should pool information. I investigate the process of finding the relevant peer group for information pooling by modelling this group formation process as dynamic network change. The simulations show that the Masses can succeed in finding the right peers, but they also suggest reasons why the Elites may often be more successful. This phenomenon generalizes to the notion of Epistemic Network Injustice. Such injustice arises when a subset of citizens is systematically deprived of connections to helpful epistemic peers, leading to their reduced political influence. Epistemic Network Injustice is a new form of epistemic injustice, related to but distinct from the notion introduced by Miranda Fricker.

Information-Pooling Bias in Collaborative Security Incident Correlation Analysis

Objective: Incident correlation is a vital step in the cybersecurity threat detection process. This article presents research on the effect of group-level information-pooling bias on collaborative incident correlation analysis in a synthetic task environment. Background: Past research has shown that uneven information distribution biases people to share information that is known to most team members and prevents them from sharing any unique information available with them. The effect of such biases on security team collaborations are largely unknown. Method: Thirty 3-person teams performed two threat detection missions involving information sharing and correlating security incidents. Incidents were predistributed to each person in the team based on the hidden profile paradigm. Participant teams, randomly assigned to three experimental groups, used different collaboration aids during Mission 2. Results: Communication analysis revealed that participant teams were 3 times more likely to discuss security incidents commonly known to the majority. Unaided team collaboration was inefficient in finding associations between security incidents uniquely available to each member of the team. Visualizations that augment perceptual processing and recognition memory were found to mitigate the bias. Conclusion: The data suggest that (a) security analyst teams, when conducting collaborative correlation analysis, could be inefficient in pooling unique information from their peers; (b) employing off-the-shelf collaboration tools in cybersecurity defense environments is inadequate; and (c) collaborative security visualization tools developed considering the human cognitive limitations of security analysts is necessary. Application: Potential applications of this research include development of team training procedures and collaboration tool development for security analysts.