Models Of Collective Decision-making Research Articles

Dynamic vaccination strategies in dual-strain epidemics: A multi-agent-based game-theoretic approach on scale-free hybrid networks

This paper proposes a multi-agent-based game-theoretic framework to explore the dynamics of decision-making among agents in a situation involving a dual-strain epidemic, where the emergence of the second strain is directly related to the first strain. It considers the impact of imperfect vaccination on dual strains within the SIRV framework, in a hybrid network that combines scale-free and grid-based structures. This research presents three models based on the level of decision-making of agents: Individual Decision Making (IDM), Collective Decision Making (CDM), and Stochastic Decision Making (SDM) model. In the IDM model, agents can continuously update their strategies based on the infection and vaccination statuses of their immediate neighbors. The CDM model offers a macro-level approach where agents’ willingness to vaccinate varies uniformly, while the SDM model maintains a constant level of willingness for vaccination across agents. This study demonstrates that agents using the IDM model tend to initiate vaccination earlier in scenarios of high infection rates, effectively mitigating the spread of the disease in subsequent epidemic seasons. However, when both infection and vaccination rates heavily influence decision-making, there is a noticeable decrease in vaccination uptake, thereby aggravating the spread of infection. Additionally, the authors found that in scenarios of high infection, an over reliance on vaccination without adequate initial uptake can jeopardize disease control efforts. Another important finding of this study is that despite the CDM and SDM models showing a quicker emergence of the second strain and reaching equilibrium points faster than the IDM model, the overall infection rate remains lower in the IDM model. This research underscores the complexity of managing dual-strain epidemics through vaccination strategies and highlights the significance of micro-level decision-making in epidemic control.

A collective decision-making model of p2p lending platforms compared to bank lending

PurposeThis study aims to develop a theoretical model that uses the decision-making theory in a financial intermediation setting to provide insights into the differences between the outcomes of the decision-making process for a bank and for a peer-to-peer (p2p) lending platform to explain the role of p2p lending versus bank lending in the credit market.Design/methodology/approachThis study develops a novel approach to explaining the differences between p2p lending and bank lending by using the decision-making theory. In particular, it analyzes the likelihood of a risky borrower being able to obtain a loan from a p2p lending platform versus the likelihood of being able to obtain a loan from a bank. The results contribute a theoretical understanding of factors that can determine the role of p2p lending platforms versus that of banks in the credit market, with implications for recovery from an economic crisis.Findingsp2p lending platforms have the potential for contributing to economic recovery when they are subject to less regulations and are able to offer a faster and less costly lending process than do banks and when they are used by a large number of lenders. However, the potential role of p2p lending platforms in recovery might be reduced when banks have access to anticyclical measures that reduce banks’ capital requirements or provide them with low-cost funds.Originality/valueThis study provides a novel approach to explaining the differences between p2p lending and bank lending by using the decision-making theory. The results contribute a theoretical understanding of factors that can determine the role of p2p lending platforms versus that of banks in the credit market.

Collective decision strategies in the presence of spatio-temporal correlations

Background: Models of collective decision-making typically assume that individuals sample information independently and decide instantaneously. In most natural and sociological settings, however, decisions occur over some timescale in which group members gather information—often from multiple sources. Information sources may persist for varying lengths of time or be viewed concurrently and identically by multiple group members. These tendencies introduce spatio-temporal correlations in gathered information with poorly understood consequences. Research Design: Here, we develop a collective decision-making model in which individuals’ access and switch between two conflicting cues that differ in their spatio-temporal properties. Results: Our model reveals that spatially and temporally correlated cues can profoundly affect collective decisions. Specifically, we observe that spatially correlated cues are dominant when individuals rarely switch between sources of information. Temporally correlated cues, on the other hand, have the strongest impact when individuals frequently switch between information sources. We also discuss how much the usage of independent information must be increased to counter the impact of correlation. Conclusions: The present model represents a first step toward more accurately capturing the complex mechanisms underlying collective decision-making in natural systems and reveals multiple ways in which the properties of environmental cues can impact collective behavior.

Hive geometry shapes the recruitment rate of honeybee colonies.

Honey bees make decisions regarding foraging and nest-site selection in groups ranging from hundreds to thousands of individuals. To effectively make these decisions, bees need to communicate within a spatially distributed group. However, the spatiotemporal dynamics of honey bee communication have been mostly overlooked in models of collective decisions, focusing primarily on mean field models of opinion dynamics. We analyze how the spatial properties of the nest or hive, and the movement of individuals with different belief states (uncommitted or committed) therein affect the rate of information transmission using spatially-extended models of collective decision-making within a hive. Honeybees waggle-dance to recruit conspecifics with an intensity that is a threshold nonlinear function of the waggler concentration. Our models range from treating the hive as a chain of discrete patches to a continuous line (long narrow hive). The combination of population-thresholded recruitment and compartmentalized populations generates tradeoffs between rapid information propagation with strong population dispersal and recruitment failures resulting from excessive population diffusion and also creates an effective colony-level signal-detection mechanism whereby recruitment to low quality objectives is blocked.

Human-Collective Collaborative Target Selection

Robotic collectives are composed of hundreds or thousands of distributed robots using local sensing and communication that encompass characteristics of biological spatial swarms, colonies, or a combination of both. Interactions between the individual entities can result in emergent collective behaviors. Human operators in future disaster response or military engagement scenarios are likely to deploy semi-autonomous collectives to gather information and execute tasks within a wide area, while reducing the exposure of personnel to danger. This article presents and evaluates two action selection models in an experiment consisting of a single human operator supervising four simulated collectives. The action selection models have two parts: (1) a best-of- n decision-making model that attempts to choose the highest-quality target from a set of n targets and (2) a quorum sensing task sequencing model that enables autonomous target site occupation. An original biologically inspired insect colony decision model is compared to a bias-reducing model that attempts to reduce environmental bias, which can negatively influence collective best-of- n decisions when poorer-quality targets are easier to evaluate than higher-quality targets. The collective decision-making models are compared in both supervised and unsupervised trials. The bias-reducing model without human supervision is slower than the original model but is 57% more accurate for decisions where evaluating the optimal target is more difficult. Human-collective teams using the bias-reducing model require less operator influence and achieve 25% higher accuracy with difficult decisions compared to the teams using the original model.

A signed network perspective on the government formation process in parliamentary democracies

In parliamentary democracies, government negotiations talks following a general election can sometimes be a long and laborious process. In order to explain this phenomenon, in this paper we use structural balance theory to represent a multiparty parliament as a signed network, with edge signs representing alliances and rivalries among parties. We show that the notion of frustration, which quantifies the amount of “disorder” encoded in the signed graph, correlates very well with the duration of the government negotiation talks. For the 29 European countries considered in this study, the average correlation between frustration and government negotiation talks ranges between 0.42 and 0.69, depending on what information is included in the edges of the signed network. Dynamical models of collective decision-making over signed networks with varying frustration are proposed to explain this correlation.

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy.

Groups of organisms, from bacteria to fish schools to human societies, depend on their ability to make accurate decisions in an uncertain world. Most models of collective decision-making assume that groups reach a consensus during a decision-making bout, often through simple majority rule. In many natural and sociological systems, however, groups may fail to reach consensus, resulting in stalemates. Here, we build on opinion dynamics and collective wisdom models to examine how stalemates may affect the wisdom of crowds. For simple environments, where individuals have access to independent sources of information, we find that stalemates improve collective accuracy by selectively filtering out incorrect decisions (an effect we call stalemate filtering). In complex environments, where individuals have access to both shared and independent information, this effect is even more pronounced, restoring the wisdom of crowds in regions of parameter space where large groups perform poorly when making decisions using majority rule. We identify network properties that tune the system between consensus and accuracy, providing mechanisms by which animals, or evolution, could dynamically adjust the collective decision-making process in response to the reward structure of the possible outcomes. Overall, these results highlight the adaptive potential of stalemate filtering for improving the decision-making abilities of group-living animals.

Division of labour promotes the spread of information in colony emigrations by the ant Temnothorax rugatulus.

The fitness of group-living animals often depends on how well members share information needed for collective decision-making. Theoretical studies have shown that collective choices can emerge in a homogeneous group of individuals following identical rules, but real animals show much evidence for heterogeneity in the degree and nature of their contribution to group decisions. In social insects, for example, the transmission and processing of information is influenced by a well-organized division of labour. Studies that accurately quantify how this behavioural heterogeneity affects the spread of information among group members are still lacking. In this paper, we look at nest choices during colony emigrations of the ant Temnothorax rugatulus and quantify the degree of behavioural heterogeneity of workers. Using clustering methods and network analysis, we identify and characterize four behavioural castes of workers-primary, secondary, passive and wandering-covering distinct roles in the spread of information during an emigration. This detailed characterization of the contribution of each worker can improve models of collective decision-making in this species and promises a deeper understanding of behavioural variation at the colony level.

Measuring satisfaction and power in influence based decision systems

We introduce collective decision-making models associated with influence spread under the linear threshold model in social networks. We define the oblivious and the non-oblivious influence models. We also introduce the generalized opinion leader–follower model (gOLF) as an extension of the opinion leader–follower model (OLF) proposed by van den Brink et al. (2011). In our model we allow rules for the final decision different from the simple majority used in OLF. We show that gOLF models are non-oblivious influence models on a two-layered bipartite influence digraph.Together with OLF models, the satisfaction and the power measures were introduced and studied. We analyze the computational complexity of those measures for the decision models introduced in the paper. We show that the problem of computing the satisfaction or the power measure is #P-hard in all the introduced models even when the subjacent social network is a bipartite graph. Complementing this result, we provide two subfamilies of decision models in which both measures can be computed in polynomial time.We show that the collective decision functions are monotone and therefore they define an associated simple game. We relate the satisfaction and the power measures with the Rae index and the Banzhaf value of an associated simple game. This will allow the use of known approximation methods for computing the Banzhaf value, or the Rae index to their practical computation.

Creation of intellectual decision-making systems in industry based on hybrid computing methods

This paper describes the promising approach in creating applied intellectual decision-making systems in industry (different types) of the new generation, based on authors synergetic collective decision-making model and methods of hybrid computational intelligence, which allows us to significantly increase the quality of the results of computer-aided solutions to complex applied problems in information variety and heterogeneity as well as to enhance decision-making by reducing losses from erroneous and individual solutions which are irrelevant to the problem complexity. There is an example of successful application of proposed methods in modeling significant class of complex application systems in medical industry, i.e. multicomponent, poorly formalized systems functioning in various types of uncertainty. The paper presents perspective approach in the field of creation of applied intellectual systems of decision-making of new generation, on the basis of author’s synergetic model of collective decision-making and methods of hybrid computing intelligence. This approach allows to significantly increase quality of results of the automated solution of complex applied challenges in the conditions of a variety and heterogeneity of information, and also to increase efficiency of decision-making due to reduction of losses from wrong and irrelevant difficulties of a task of individual decisions that results of laboratory experiments confirm.

Non-Congruent Views about Signal Precision in Collective Decisions

Abstract We relax the standard assumptions in collective decision-making models that voters can not only derive a perfect view about the accuracy of the information at their disposal before casting their votes, but can, in addition, also correctly assess other voters’ views about it. We assume that decision-makers hold potentially differing views, while remaining ignorant about such differences, if any. In this setting, we find that information aggregation works well with voting rules other than simple majority: as voters vote less often against their information than in conventional models, they can deliver higher-quality decisions, including in the canonical 12 jurors case. We obtain voting equilibria with many instances, in which other voting rules, including unanimity, clearly outperform simple majority.

Opinion Dynamics With Mobile Agents: Contrarian Effects by Spatial Correlations.

We investigate the dynamics of opinion formation in a group of mobile agents with noisy perceptions. Two models are applied, the 2-state Galam opinion dynamics model with contrarians and an urn model of collective decision-making. It is shown that models built on the well-mixed assumption fail to represent the dynamics of a simple scenario. The challenge of accounting for correlations in the agents' spatial distribution is overcome by different heuristics and supported by empirical investigations. We present a concise, simple 1-dimensional macroscopic modeling approach that can be tuned to correctly model spatial correlations.

Self-Organizing Control Mechanism Based on Collective Decision-Making for Information Uncertainty

Because of the rapid growth in the scale and complexity of information networks, self-organizing systems are increasingly being used to realize novel network control systems that are highly scalable, adaptable, and robust. However, the uncertainty of information (with regard to incompleteness, vagueness, and dynamics) in self-organizing systems makes it difficult for them to work appropriately in accordance with the network state. In this study, we apply a model of the collective decision-making of animal groups to enable self-organizing control mechanisms to adapt to information uncertainty. Specifically, we apply a mathematical model of collective decision-making that is known as the effective leadership model (ELM). In the ELM, informed individuals (those who are experienced or well-informed) take the role of leading the others. In contrast, uninformed individuals (those who perceive only local information) follow neighboring individuals. As a result of the collective behavior of informed/uninformed individuals, the animal group achieves consensus. We consider a self-organizing control mechanism using potential-based routing with an optimal control, and propose a mechanism for determining a data-packet forwarding scheme based on the ELM. Through evaluation by simulation, we show that, in a situation in which the perceived information is incomplete and dynamic, nodes can forward data packets in accordance with the network state by applying the ELM.

Self-Enforcing Partisan Procedures

Using a multi-stage model of collective decision-making, we study how partisan polarization in legislatures affects the initial choice and future revision of procedural rules. We analyze how a legislature allocates proposal power over policy decisions under uncertainty about the future polarization in policy preferences among legislators. Our notion of polarization encompasses both intra- and inter-party heterogeneity in policy preferences. We show that majority procedures are partisan and self-enforcing, i.e., survive future revisions even though they concentrate proposal power in the hands of a few (non-median) majority party members. This leads to systematic pro-majority policy bias. Increased polarization, however, may not increase the concentration of proposal power within the majority party. The model provides a unified analytical framework to evaluate a long-standing debate about the foundations of partisan influence in the U.S. Congress.

Conformity Preferences and Information Gathering Effort in Collective Decision Making

AbstractOur study concerns a collective decision-making model for the collection of information from two voters. Both voters, who tend to make the same voting choices because of their conformity preferences, collect information about the consequences of a project and then vote on the project. We focus on an informative equilibrium in which voters vote informatively using pure strategies. This is a symmetric Nash equilibrium. Our result is interesting as it shows that nonconformist voters exert less effort from a social perspective because of a positive externality that results in the free-rider problem, while conformity preferences can help to improve the sum of the voters’ expected payoffs from the social perspective. This is because conformity preferences may alleviate the free-rider problem associated with coordination (making the same vote). Specifically, conformity preferences give special importance to the correlation between voters’ signals, even if this correlation is unrelated to the accuracy of the signals. Furthermore, we present the exact conformity preference level which helps voters to exert an optimal effort level that maximizes the sum of the voters’ expected payoffs compared to the nonconformist case. In addition, we graphically illustrate comparative statics on effort levels in informative equilibria.

Analyzing Social Roles Based on a Hierarchical Model and Data Mining for Collective Decision-Making Support

With the popularity of social networking services (SNSs) and the increase of users, individuals' social roles in a social network have become more and more important in terms of the recommendation of personalized services and the collective decision-making process. Usually, in an SNS system, active users may not represent the major opinions among the whole users, and most of the users' opinions may be multifarious. In this paper, we focus on analyzing and identifying users' dynamical social roles to facilitate the collective decision-making process. After introducing the social choice theory and an improved collective decision-making model, we present a three-layer model to analyze users' social roles in a hierarchical way and develop an integrated mechanism to utilize the identification of social roles to support the collective decision making. Based on a developed NetLogo-based tool, a case study for the course-offering determination with an application scenario is demonstrated to show the process of using users' social roles to support the collective decision making. The comparison experiment conducted between our method and the Delphi method shows the usefulness of our proposed method to help users achieve the decision consensus in a more efficient way.

Kinetic models of collective decision-making in the presence of equality bias

We introduce and discuss kinetic models describing the influence of the competence in the evolution of decisions in a multi-agent system. The original exchange mechanism, which is based on the human tendency to compromise and change opinion through self-thinking, is here modified to include the role of the agents' competence. In particular, we take into account the agents' tendency to behave in the same way as if they were as good, or as bad, as their partner: the so-called equality bias. This occurred in a situation where a wide gap separated the competence of group members. We discuss the main properties of the kinetic models and numerically investigate some examples of collective decision under the influence of the equality bias. The results confirm that the equality bias leads the group to suboptimal decisions.

Success and Failure of Parliamentary Motions: A Social Dilemma Approach.

Parliamentary motions are a vital and frequently used element of political control in democratic regimes. Despite their high incidence and potential impact on the political fate of a government and its policies, we know relatively little about the conditions under which parliamentary motions are likely to be accepted or rejected. Current collective decision-making models use a voting power framework in which power and influence of the involved parties are the main predictors. We propose an alternative, social dilemma approach, according to which a motion’s likelihood to be accepted depends on the severity of the social dilemma underlying the decision issue. Actor- and dilemma-centered hypotheses are developed and tested with data from a stratified random sample of 822 motions that have been voted upon in the Dutch Parliament between September 2009 and February 2011. The social dilemma structure of each motion is extracted through content coding, applying a cognitive mapping technique developed by Anthony, Heckathorn and Maser. Logistic regression analyses are in line with both, actor-centered and social-dilemma centered approaches, though the latter show stronger effect sizes. Motions have a lower chance to be accepted if voting potential is low, the proposer is not from the voting party, and if the problem underlying the motion reflects a prisoner’s dilemma or a pure competition game as compared to a coordination game. The number of proposing parties or a battle of the sexes structure does not significantly affect the outcome.

Individual-level personality influences social foraging and collective behaviour in wild birds.

There is increasing evidence that animal groups can maintain coordinated behaviour and make collective decisions based on simple interaction rules. Effective collective action may be further facilitated by individual variation within groups, particularly through leader–follower polymorphisms. Recent studies have suggested that individual-level personality traits influence the degree to which individuals use social information, are attracted to conspecifics, or act as leaders/followers. However, evidence is equivocal and largely limited to laboratory studies. We use an automated data-collection system to conduct an experiment testing the relationship between personality and collective decision-making in the wild. First, we report that foraging flocks of great tits (Parus major) show strikingly synchronous behaviour. A predictive model of collective decision-making replicates patterns well, suggesting simple interaction rules are sufficient to explain the observed social behaviour. Second, within groups, individuals with more reactive personalities behave more collectively, moving to within-flock areas of higher density. By contrast, proactive individuals tend to move to and feed at spatial periphery of flocks. Finally, comparing alternative simulations of flocking with empirical data, we demonstrate that variation in personality promotes within-patch movement while maintaining group cohesion. Our results illustrate the importance of incorporating individual variability in models of social behaviour.

To elect or to appoint? Bias, information, and responsiveness of bureaucrats and politicians

In this paper, we address empirically the trade-offs involved in choosing between bureaucrats and politicians. In order to do this, we map institutions of selection and retention of public officials to the type of public officials they induce. We do this by specifying a collective decision-making model, and exploiting its equilibrium information to obtain estimates of the unobservable types. We focus on criminal decisions across US states' Supreme Courts. We find that justices that are shielded from voters' influence (“bureaucrats”) on average (i) have better information, (ii) are more likely to change their preconceived opinions about a case, and (iii) are more effective (make less mistakes) than their elected counterparts (“politicians”). We evaluate how performance would change if the courts replaced majority rule with unanimity rule.