Homogeneous Agents Research Articles

Capturing multi-stage fuzzy uncertainties in hybrid system dynamics and agent-based models for enhancing policy implementation in health systems research.

BackgroundIn practical research, it was found that most people made health-related decisions not based on numerical data but on perceptions. Examples include the perceptions and their corresponding linguistic values of health risks such as, smoking, syringe sharing, eating energy-dense food, drinking sugar-sweetened beverages etc. For the sake of understanding the mechanisms that affect the implementations of health-related interventions, we employ fuzzy variables to quantify linguistic variable in healthcare modeling where we employ an integrated system dynamics and agent-based model.MethodologyIn a nonlinear causal-driven simulation environment driven by feedback loops, we mathematically demonstrate how interventions at an aggregate level affect the dynamics of linguistic variables that are captured by fuzzy agents and how interactions among fuzzy agents, at the same time, affect the formation of different clusters(groups) that are targeted by specific interventions.ResultsIn this paper, we provide an innovative framework to capture multi-stage fuzzy uncertainties manifested among interacting heterogeneous agents (individuals) and intervention decisions that affect homogeneous agents (groups of individuals) in a hybrid model that combines an agent-based simulation model (ABM) and a system dynamics models (SDM). Having built the platform to incorporate high-dimension data in a hybrid ABM/SDM model, this paper demonstrates how one can obtain the state variable behaviors in the SDM and the corresponding values of linguistic variables in the ABM.ConclusionsThis research provides a way to incorporate high-dimension data in a hybrid ABM/SDM model. This research not only enriches the application of fuzzy set theory by capturing the dynamics of variables associated with interacting fuzzy agents that lead to aggregate behaviors but also informs implementation research by enabling the incorporation of linguistic variables at both individual and institutional levels, which makes unstructured linguistic data meaningful and quantifiable in a simulation environment. This research can help practitioners and decision makers to gain better understanding on the dynamics and complexities of precision intervention in healthcare. It can aid the improvement of the optimal allocation of resources for targeted group (s) and the achievement of maximum utility. As this technology becomes more mature, one can design policy flight simulators by which policy/intervention designers can test a variety of assumptions when they evaluate different alternatives interventions.

A Three-Choice Minority Game Model with Homogeneous Agent Preferences for Resource Allocation

Microgrids have a promising potential to provide off-grid energy access for rural communities. With the growing interest on microgrids and energy access, there has been a renewed focus on developing demand management schemes appropriate for simple microgrids. One basic demand management concern is the problem of shaping consumer demand profiles such that peaks and troughs can be minimized. This problem can be viewed as a simplified resource allocation problem where each resource choice can only be chosen by a fixed number of agents. This study models the described allocation problem as a three-choice minority game where the resources available to the agents map to energy time-of-use windows. Agents were defined to have a homogeneous preference over these available choices of time windows. Different cost functions were likewise simulated in the game. The specific cost functions used in the simulations were chosen to determine whether agents can be influenced to self-organize such that the total number of agents per time window falls within a defined capacity. Results show that some cost functions successfully maintain agent numbers within the defined capacity. Nevertheless, results also indicate that agent gains may suffer when the capacity per choice is met.

Private versus public old-age security

We directly compare two institutions, a family compact—a parent makes a transfer to her parent in anticipation of a possible future gift from her children—with a pay-as-you-go, public pension system, in a life cycle model with endogenous fertility wherein children are valued both as consumption and investment goods. Absent intragenerational heterogeneity, we show that a benevolent government has no welfare justification for introducing public pensions alongside thriving family compacts since the former is associated with inefficiently low fertility. This result hinges critically on a fiscal externality—the inability of middle age agents to internalize the impact of their fertility decisions on old-age transfers under a public pension system. With homogeneous agents, a strong-enough negative aggregate shock to middle-age incomes destroys all family compacts, and in such a setting, an optimal public pension system cannot enter. This suggests the raison d’etre for social security must lie outside of its function as a pension system—specifically its redistributive function which emerges with heterogeneous agents. In a simple modification of our benchmark model—one that allows for idiosyncratic frictions to compact formation such as differences in infertility/mating status—a welfare-enhancing role for a public pension system emerges; such systems may flourish even when family compacts cannot.

Clonal plasticity: an autonomic mechanism for multi-agent systems to self-diversify

Diversity has long been used as a design tactic in computer systems to achieve various properties. Multi-agent systems, in particular, have utilized diversity to achieve aggregate properties such as efficiency of resource allocations, and fairness in these allocations. However, diversity has usually been introduced manually by the system designer. This paper proposes a decentralized technique, clonal plasticity, that makes homogeneous agents self-diversify, in an autonomic way. We show that clonal plasticity is competitive with manual diversification, at achieving efficient resource allocations and fairness.

Multiparty Consensus of Linear Heterogeneous Multiagent Systems

In this paper, we investigate the multiparty output consensus of linear heterogeneous agents, in which the outputs of the agents within a party synchronize to each other, but different parties have different synchronized trajectories. To solve this problem, graphs with complex edge weights are used. We provide a sufficient condition for leader-follower multiparty output consensus of linear heterogeneous agents. We also give a similar sufficient condition for multiparty state consensus of linear homogeneous agents as homogeneous agents could be considered as a special case of heterogeneous agents. As one direct application of our theory, we provide a solution to formation control of linear heterogeneous agents, where the position of each follower agent in the formation is described by two key pieces of information: A fixed orientation of the follower's location with respect to the leader's location (within a given coordinate system), and a time-variant formation constraint (such as the relative distance) between the leader and the follower, which is captured by some linear command generator. We present sufficient conditions for both simultaneous formation and reference trajectory tracking via the internal model principle.

Modelling UK sub-sector industrial energy demand

The importance of considering homogenous economic agents when estimating energy demand functions is recognized in the literature, but so far data availability problems have explained the prevalence of empirical analyses only at an aggregate level. Motivated by the goal of developing the new industrial module to be adopted by the UK government Department of Business, Energy and Industrial Strategy (BEIS) for their econometric Energy Demand Model, we propose the first cointegration analysis that provides evidence on energy demand elasticities with respect to economic activity and energy price at a disaggregated industrial level. While the average of our estimates are comparable to those of the existing literature on the industrial sector as a whole, we find that there is considerable heterogeneity in relation to the long-run impact of economic activity and energy price on energy consumption, as well as to the speed with which firms re-adjust their equilibrium demand of energy in response to economic shocks. Finally, we learn that long-run disequilibria are tackled through altering the level of energy consumption rather than economic activity, a conclusion that has important implications for policy analysis.

An Iterative Method for Control Gain Design of Multiagent Systems With Process Noise

This brief aims to optimize the consensus performance of multiple homogeneous agents, each of which is governed by a general discrete-time linear system with white process noise, exchange state information with its neighboring agents according to an undirected communication topology, and generates its local control in a linear way. The common control gain of agents determines the consensus performance, which is measured by the ultimate mean square deviation of the states of agents. The consensus performance optimization with respect to the control gain takes a nonlinear matrix inequality form and is difficult to solve. To handle this nonlinearity issue, this brief proposes an iterative method. At each iteration, a descent direction of the control gain is computed by solving two linear matrix inequality optimizations based on a given feasible control gain. Then, a line search algorithm is implemented to move the control gain along the obtained descent direction to improve the consensus performance. That updated control gain will work as the starting feasible control gain of the next iteration. This method can well handle the nonlinearity of the original consensus performance optimization and efficiently improve the consensus performance, which is confirmed by simulations.

About disconnected topologies and synchronization of homogeneous nonlinear agents over switching networks

SummaryIn this paper, we study the problem of networks on nonlinear oscillators when communication topologies are disconnected. In the first part of this paper, we show that the high‐gain design procedure succeeds in achieving clustered consensus when disconnected topologies occur. In the second part, we study a network of nonlinear oscillators when the topology is switching between sets of disconnected and connected topologies. By a Lyapunov analysis, we prove that if disconnected topologies last for a limited amount of time and the switching law for connected topologies fulfills an average dwell‐time condition, synchronization is achieved.

Peer effects in endogenous networks

This paper presents a model of strategic network formation with local complementarities in effort levels and positive local externalities. Results are obtained for a general class of payoff functions, which subsumes the linear-quadratic specification frequently used in theoretical and applied work. We assume homogeneous agents and characterize equilibria for two-sided and one-sided link formation. (Pairwise) Nash equilibrium networks are nested split graphs, which are a strict subset of core-periphery networks. We highlight the relevance of the convexity of the value function for obtaining these structures. More central agents are shown to exert more effort and obtain higher gross payoffs in equilibrium. However, net of linking cost, central agents may obtain strictly lower net payoffs. The curvature of the value function is also important for efficiency considerations. These findings are relevant for many social and economic phenomena, such as educational attainment, criminal activity, labor market participation, and R&D expenditures of firms.

Aggregate Uncertainty Can Lead to Incorrect Herds

A continuum of homogeneous rational agents choose between two competing technologies. Agents observe a private signal and sample others' previous choices. Signals have an aggregate component of uncertainty, so aggregate behavior does not necessarily reflect the true state of nature. Nonetheless, agents still find others' choices informative, and base their decisions partly on others' behavior. Consequently, bad choices can be perpetuated: aggregate uncertainty makes agents herd on the inferior technology with positive probability. I derive the optimal decision rule when agents sample exactly two individuals. I also present examples with herds on the inferior technology for arbitrarily large sample sizes. (JEL C72, D83)

Inequity-aversion and relative kindness intention jointly determine the expenditure of effort in project teams.

The literature on team cooperation has neglected the effects of relative kindness intention on cooperation, which we measure by comparing the kindness intentions of an agent to her group members to the kindness shown by other members to this same agent. We argue that the agent’s emotional reaction to material payoff inequity is not constant, but rather affected by her relative kindness intention. Then, we apply the model to team projects with multiple partners and investigate how inequity-aversion and relative kindness intention jointly influence team cooperation. We first consider the case of homogeneous agents, where their marginal productivity levels and technical capacities are the same, and then consider the case of heterogeneous agents, where their marginal productivity levels and technical capacities are not the same. Our results show that inequity-aversion has no effect on effort expenditure in the former case, but does affect it in the latter case. The consideration of relative kindness intention may impact the agents’ optimal cooperative effort expenditure when their technical capacities are different. In addition, it is beneficial for team cooperation, and might not only reduce the negative impact but also enhance the positive impact of inequity-aversion on the agents’ effort expenditures.

$H_{2}$ Optimal Coordination of Homogeneous Agents Subject to Limited Information Exchange

Controllers with a diagonal-plus-low-rank structure constitute a scalable class of controllers for multi-agent systems. Previous research has shown that diagonal-plus-low-rank control laws appear as the optimal solution to a class of multi-agent $H_{2}$ coordination problems, which arise in the control of wind farms. In this technical note we show that this result extends to the case where the information exchange between agents is subject to limitations. We also show that the computational effort required to obtain the optimal controller is independent of the number of agents and provide analytical expressions that quantify the usefulness of information exchange.

Finite‐Time Consensus Problem for Second‐Order Multi‐Agent Systems Under Switching Topologies

AbstractThis paper investigates the finite‐time consensus problem for multi‐agent systems with second‐order individual dynamics under switching topologies. A distributed continuous‐time protocol is designed to guarantee finite‐time consensus for homogeneous agents without predetermined leaders, i.e., it ensures agents asymptotically converge to an average consensus within finite time, even if the interaction topology among them is time‐varying but stepwise jointly‐connected. In particular, it introduces a distributed continuous‐time protocol to reach consensus in finite time and reduce the chattering together. Finally, the simulation results are also given to validate the proposed approach.

Assessment of micronecrotic tumor tissue using dynamic contrast-enhanced magnetic resonance imaging

Compartmental models for evaluation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) datasets assume a homogeneous interstitital volume distribution and homogeneous contrast agent (CA) distribution within each compartment, neglecting effects of CA diffusion within the compartments. When necrotic or micronecrotic tumor tissue is present, these assumptions may no longer be valid. Therefore, the present study investigates the validity of three compartmental models in assessing tumors with necrotic components.The general diffusion equation for inhomogeneous tissue was used to simulate the extravasation of a low-molecular-weight contrast agent from a feeding vessel into the interstitial space. The simulated concentration-time curves were evaluated using the extended Tofts model, a parallel 3-compartment model, and a sequential 3-compartment model.The extended Tofts model overestimated the interstitial volume fraction by a median of 6.9% resp. 10.0% and the parallel 3-compartment model by 8.6% resp. 15.5%, while the sequential 3-compartment model overestimated it by 0.2% resp. underestimated it by 18.8% when simulating a mean vessel distance of 100μm resp. 150μm. Overall, the sequential 3-compartment model provided more reliable results both for the total fractional interstitial volume and for the interstitial subcompartments.

Task and Motion Coordination for Heterogeneous Multiagent Systems With Loosely Coupled Local Tasks

We consider a multiagent system that consists of heterogeneous groups of homogeneous agents. Instead of defining a global task for the whole team, each agent is assigned a local task as syntactically cosafe linear temporal logic formulas that specify both motion and action requirements. Interagent dependence is introduced by collaborative actions, of which the execution requires multiple agents’ collaboration. To ensure the satisfaction of all local tasks without central coordination, we propose a bottom–up motion and task coordination strategy that contains an off-line initial plan synthesis and an online coordination scheme based on real-time exchange of request and reply messages. It facilitates not only the collaboration among heterogeneous agents but also the task swapping between homogeneous agents to reduce the total execution cost. It is distributed as any decision is made locally by each agent based on local computation and communication within neighboring agents. It is scalable and resilient to agent failures as the dependence is formed and removed dynamically based on agent capabilities and their plan execution status, instead of preassigned agent identities. The overall scheme is demonstrated by a simulated scenario of 20 agents with loosely coupled local tasks.

In situ synthesis of Al2O3‐supported ZnCr2O4 nanoparticles for application as an activated photocatalyst

AbstractZincochromite nanoparticles (NPs) were precipitated on surfaces of the as‐prepared Al2O3 micron‐sized particles by a heterogeneous precipitation technique using urea as a homogeneous precipitation agent. This procedure leads to decrease the pore diameter and increase the pore volume and specific surface area (as), realizing the potential access to ZnCr2O4 catalytic sites. Although the obtained band gap energy (Eg) of Al2O3‐ZnCr2O4 composite is about 2.3 eV (more than ZnCr2O4), the absorbance is enhanced about 1.5 orders of magnitude. These characteristics make it an effective photocatalyst of inorganic dyes from an aqueous media. Dye removal performance of the nanocomposite powder is higher than that of pure ZnCr2O4, which is attributed to an increase in the photocatalytic sites and the absorbance intensity. It was believed that the surface area created from Al2O3 support realized the potential access to ZnCr2O4 catalytic sites. To confirm these assertions, X‐ray diffractometry (XRD), scanning electron microscopy (SEM), diffuse reflectance spectroscopy (DRS), and N2 adsorption‐desorption analysis were applied.

On the Construction of High Dimensional Simple Games

Voting is a commonly applied method for the aggregation of the preferences of multiple agents into a joint decision. If preferences are binary, i.e., “yes” and “no”, every voting system can be described by a (monotone) Boolean function : f0; 1gn ! f0; 1g. However, its naive encoding needs 2n bits. The subclass of threshold functions, which is sufficient for homogeneous agents, allows a more succinct representation using n weights and one threshold. For heterogeneous agents one can represent as an intersection of k threshold functions. Taylor and Zwicker have constructed a sequence of examples requiring k 2 n2 ?1 and provided a construction guaranteeingk ? n bn=2c 2 2n?o(n). The magnitude of the worst case situation was thought to be determined by Elkind et al. in 2008, but the analysis unfortunately turned out to be wrong. Here we uncover a relation to coding theory that allows the determination of the minimum number k for a subclass of voting systems. As an application, we give a construction for k 2n?o(n), i.e., there is no gain from a representation complexity point of view.

A novel gain design method to improve the consensus performance of output-feedback multi-agent systems

This paper considers the consensus problem of a group of homogeneous agents. These agents are governed by a general linear system and can only directly measure the output, instead of the state. In order to achieve the consensus goal, each agent estimates its state through a Luenberger observer, exchanges its estimated state with neighbors, and constructs the control input with the estimated states of its own and neighbors. Due to the existence of observation and process noises, only practical consensus, instead of asymptotical consensus, can be achieved in such multi-agent systems. The performance of the achieved practical consensus can be measured by the ultimate mean square deviation of the states of agents. That performance is closely related to the observation gains of the state observers and the control gains of agents. This paper proposes a method to optimize such performance with respect to the concerned observation and control gains. That method starts with a set of feasible observation and control gains and formulates a group of linear matrix inequalities (LMIs). Solving these LMIs gives some intermediate matrix variables. By perturbing observation and control gains, and the intermediate matrix variables, the original LMIs yield another group of LMIs, which can be solved to provide a descent direction of observation and control gains. Moving along that descent direction, observation and control gains can be improved to yield better performance and work as the starting point of the next iteration. By iteratively repeating this procedure, we can hopefully improve the consensus performance of the concerned multi-agent system. Simulations are done to demonstrate the effectiveness of the proposed method.

On the Bipartite Consensus for Generic Linear Multiagent Systems With Input Saturation

The bipartite consensus problem for a group of homogeneous generic linear agents with input saturation under directed interaction topology is examined. It is established that if each agent is asymptotically null controllable with bounded controls and the interaction topology described by a signed digraph is structurally balanced and contains a spanning tree, then the semi-global bipartite consensus can be achieved for the linear multiagent system by a linear feedback controller with the control gain being designed via the low gain feedback technique. The convergence analysis of the proposed control strategy is performed by means of the Lyapunov method which can also specify the convergence rate. At last, the validity of the theoretical findings is demonstrated by two simulation examples.

Optimizing systematic technology adoption with heterogeneous agents

The traditional operational optimization models of systematic technology adoption commonly assume the existence of a global social planner and ignore the existence of heterogeneous decision makers who interact with each other. This paper develops a stylized (or conceptual) optimization model of systematic technology adoption with heterogeneous agents (i.e., decision makers) and uncertain technological learning. Each agent attempts to identify optimal solutions to adopting technologies for a portion of the entire system. The agents in the model have different foresight and different risk attitudes and interact with one another in terms of technological spillover.This paper first illustrates that although a well recalibrated representative model can perform well enough when the interest is placed on aggregate variables, it could react to a policy (a carbon tax in this paper) differently from the multi-agent model. Then this paper explores how the agents’ heterogeneities and interactions affect the optimal solutions of systematic technology adoption. The main findings of the study are that (1) the existence of multiple agents implies a slower adoption of advanced technologies in the entire system than assuming the existence of a global social planner, (2) with homogeneous agents, technological spillover tends to enhance the lock-in effect on previous technologies, and (3) with heterogeneous agents, even a small technological spillover rate can significantly accelerate the adoption of the advanced technology.