Agents Update Research Articles

Oposición, asociación y reformismo: la acción colectiva de los médicos en Colombia (1990-2015)

En este trabajo se analizan los cambios que se presentaron en el ejercicio de la profesión médica y en sus condiciones laborales como resultado de un conjunto de transformaciones sociales (en el trabajo), científicas (en la profesión) y legales (reformas estructurales al sistema de salud y a la regulación laboral); así como las principales acciones colectivas que desarrolló una parte considerable del gremio médico en Colombia para enfrentar esos cambios, entre 1990 y el 2015. Se utilizan dos perspectivas teóricas: primero, el enfoque de la cultura laboral que considera la relación entre trabajo y cultura como un proceso en el que los agentes actualizan su cultura en contextos y realidades de poder específicos, en medio de procesos de conflictos y negociaciones que ocurren en las interacciones de los sujetos. Segundo, la teoría de la acción colectiva y su enfoque de contienda política, entendida como la interacción pública y colectiva entre diferentes actores, en una relación de conflicto, en la que el gobierno es una de las partes. Los resultados de la contienda afectan los intereses de una de las partes, según las reivindicaciones que logren imponerse. Tres momentos caracterizaron las acciones colectivas de los médicos: las protestas sociales en oposición a las reformas a la salud y para reivindicar sus derechos laborales; la asociación para buscar la unidad del gremio médico y tener un solo interlocutor ante el Gobierno; y la utilización del derecho como un recurso para promover modificaciones en la regulación del sistema de salud y de la práctica médica. A través de sus acciones colectivas, un sector del gremio médico se situó como un actor político emergente que, al tiempo que luchó por sus intereses, también participó como protagonista clave en la configuración del campo de la salud en Colombia en el contexto de las reformas al sistema de salud. Así mismo, a través de aquellas, los médicos lograron resistir, moldear o filtrar los efectos de los cambios en su cultura laboral.

Asynchronous Agreement through Distributed Coordination Algorithms Associated with Periodic Matrices

For the widely studied consensus-type distributed multi-agent algorithms, a standard discrete-time model is a linear system whose system matrix is stochastic, thus implementing the “averaging” updating rule for each agent. To ensure agreement among all the agents asymptotically, one usually requires the stochastic matrix to be indecomposable and aperiodic (SIA). In this paper, we show that in practice such requirements can be relaxed by allowing the matrix to be periodic if the agents update asynchronously. Such a relaxation is somewhat surprising since for synchronous updating, periodic matrices in general give rise to oscillations in the agents’ states. The key step to prove reaching agreement is to use a stochastic Lyapunov function to prove almost sure convergence of the associated stochastic linear system. The results reveal the critical role that asynchrony may play for distributed network algorithms.

Event‐triggered consensus of non‐linear multi‐agent systems with sampling data and time delay

The sampled-data based event-triggered strategy for consensus of leader-following multi-agent systems with the input-delay and non-linear dynamics is studied. A novel event-triggered transmission strategy is proposed, where the controller of each agent updates and the sampled-data of each agent transmits to its neighbours' only at the agent's own triggering time instants. The measurement errors are defined based on the state information of the local neighbourhood and leader to design the event triggering scheme, and this scheme reduces the number of event triggering times, the controller updating and communications between agents. Moreover, in the proposed event triggered control strategy, the Zeno-behaviour is avoided. The control protocol and event triggering scheme can be simply designed by solving an LMI condition to achieve leader-following consensus asymptotically. The effectiveness of the proposed results is demonstrated via a numerical example.

SMOOTHING-BASED INITIALIZATION FOR LEARNING-TO-FORECAST ALGORITHMS

Under adaptive learning, recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes, these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual data availability restrictions of macroeconomics. To circumvent this issue, we propose a new smoothing-based initialization routine that optimizes the use of a training sample of data to obtain initials consistent with the statistical properties of the learning algorithm. Our method is generically formulated to cover different specifications of the learning mechanism, such as the least-squares and the stochastic gradient algorithms. Using simulations, we show that our method is able to speed up the convergence of initial estimates in exchange for a higher computational cost.

Inattentive agents and disagreement about economic activity

This paper evaluates empirically the (in)consistency of disagreement in survey forecasts with the prediction of sticky information models à la Mankiw-Reis, in which only a fraction of agents update their information sets at every period. To address this issue, a dynamic stochastic general equilibrium (DSGE) model that features agents’ infrequent information updating as well as nominal rigidities is fit to U.S. data. We find that the survey disagreement shares two pivotal characteristics with its model-based counterparts: (i) disagreement can be predicted by agents’ average forecast revisions reflecting the arrival of shocks; and (ii) disagreement exhibits a U-shaped relationship against the deviation of output growth from its steady state. These features arise because the arrival of new information elevates disagreement among informed and uninformed agents. Our findings indicate a substantial degree of infrequent information updating in the survey disagreement. The existing literature often uses survey disagreement as a proxy for macroeconomic uncertainty, but our finding suggests that it is unlikely to be an appropriate measure.

Learning in Social Networks

This survey covers models of how agents update behaviors and beliefs using information conveyed through social connections. We begin with sequential social learning models, in which each agent makes a decision once and for all after observing a subset of prior decisions; the discussion is organized around the concepts of diffusion and aggregation of information. Next, we present the DeGroot framework of average-based repeated updating, whose long- and medium-run dynamics can be completely characterized in terms of measures of network centrality and segregation. Finally, we turn to various models of repeated updating that feature richer optimizing behavior, and conclude by urging the development of network learning theories that can deal adequately with the observed phenomenon of persistent disagreement.

Simulating Fire-Sales in a Banking and Shadow Banking System

We develop an agent based model of traditional banks and asset managers. Our aim is to investigate the channels of contagion of shocks to asset prices within and between the two financial sectors, including the effects of fire sales and their impact on financial institutions’ balance sheets. We take a structural approach to the price formation mechanism as in Bluhm, Faia and Kranen (2014) and introduce a clearing mechanism with an endogenous formation of asset prices. Both types of institutions hold liquid and illiquid assets and are funded via equity and deposits. Traditional banks are interconnected in the money market via mutual interbank claims, where the rate of return is endogenously determined through a tatonnement process. We show how in such a set-up an initial exogenous liquidity shock may lead to a fire-sale spiral. Banks, which are subject to capital and liquidity requirements, may be forced to sell an illiquid security, which impacts its, endogenously determined, market price. As the price of the security decreases, both agents update their equity and adjust their balance sheets by making decisions on whether to sell or buy the security. This endogenous process may trigger a cascade of sales leading to a fire-sale. We find that, first, mixed portfolios banks act as plague-spreader in a context of financial distress. Second, higher bank capital requirements may aggravate contagion since they may incentivise banks to hold similar assets, and choose mixed portfolios business model which is also characterized by lower levels of voluntary capital buffer. Third, asset managers absorb small liquidity shocks but they exacerbate contagion when liquid buffers are fully utilised.

Information Flows, the Accuracy of Opinions, and Crashes in a Dynamic Network

Markets coordinate the flow of information in the economy, aggregating it through the price mechanism. We develop a dynamic model of information transmission and aggregation in financial and other social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity in which agents’ opinions are widely dispersed. We add varying degrees of homophily through a model parameter called tribal preference and find that crash frequency is decreasing in the degree of homophily. Our results suggest that the information aggregation function of markets can fail solely because of the dynamics of information flows, irrespective of shocks or news.

Secure consensus control for multiagent systems with attacks and communication delays

This paper addresses the consensus problem for nonlinear multi-agent systems suffering from attacks and communication delays. The network studied in this paper consists of two types of agents, namely, loyal agents and attack agents. The loyal agents update their states based on delayed state information exchanged with their neighbors. Meanwhile, the attack agents can strategically send messages with wrong values, or collude with other attack agents to disrupt the correct operation of the system. We design a novel delay robust secure consensus U+0028 DRSC U+0029 algorithm according to the neighboring nodes U+02BC delayed information. Convergence analysis of the system under the protocol designed is provided by using Lyapunov-Krasovskii stability theory and Barbalat-like argument approach. Finally, an example and simulation results are presented to demonstrate the effectiveness of the algorithm.

Consensus in the Presence of Multiple Opinion Leaders: Effect of Bounded Confidence

The problem of analyzing the performance of networked agents exchanging evidence in a dynamic network has recently grown in importance. This problem has relevance in signal and data fusion network applications and in studying opinion and consensus dynamics in social networks. Due to its capability of handling a wider variety of uncertainties and ambiguities associated with evidence, we use the framework of Dempster-Shafer (DS) theory to capture the opinion of an agent. We then examine the consensus among agents in dynamic networks in which an agent can utilize either a cautious or receptive updating strategy. In particular, we examine the case of bounded confidence updating where an agent exchanges its opinion only with neighboring nodes possessing 'similar' evidence. In a fusion network, this captures the case in which nodes only update their state based on evidence consistent with the node's own evidence. In opinion dynamics, this captures the notions of Social Judgment Theory (SJT) in which agents update their opinions only with other agents possessing opinions closer to their own. Focusing on the two special DS theoretic cases where an agent state is modeled as a Dirichlet body of evidence and a probability mass function (p.m.f.), we utilize results from matrix theory, graph theory, and networks to prove the existence of consensus agent states in several time-varying network cases of interest. For example, we show the existence of a consensus in which a subset of network nodes achieves a consensus that is adopted by follower network nodes. Of particular interest is the case of multiple opinion leaders, where we show that the agents do not reach a consensus in general, but rather converge to 'opinion clusters'. Simulation results are provided to illustrate the main results.

A consensus opinion model based on the evolutionary game

We propose a consensus opinion model based on the evolutionary game. In our model, both of the two connected agents receive a benefit if they have the same opinion, otherwise they both pay a cost. Agents update their opinions by comparing payoffs with neighbors. The opinion of an agent with higher payoff is more likely to be imitated. We apply this model in scale-free networks with tunable degree distribution. Interestingly, we find that there exists an optimal ratio of cost to benefit, leading to the shortest consensus time. Qualitative analysis is obtained by examining the evolution of the opinion clusters. Moreover, we find that the consensus time decreases as the average degree of the network increases, but increases with the noise introduced to permit irrational choices. The dependence of the consensus time on the network size is found to be a power-law form. For small or larger ratio of cost to benefit, the consensus time decreases as the degree exponent increases. However, for moderate ratio of cost to benefit, the consensus time increases with the degree exponent. Our results may provide new insights into opinion dynamics driven by the evolutionary game theory.

The Curse of Long Horizons

We study dynamic moral hazard with symmetric ex ante uncertainty about the difficulty of the job. The principal and agent update their beliefs about the difficulty as they observe output. Effort is private and the principal can only offer spot contracts. The agent has an additional incentive to shirk beyond the disutility of effort when the principal induces effort: shirking results in the principal having incorrect beliefs. We show that the effort inducing contract must provide increasingly high powered incentives as the length of the relationship increases. Thus it is never optimal to always induce effort in very long relationships.

Agent-Based Distributed Security Constrained Optimal Power Flow

The need for the coordination of distributed energy resources in the future electric power system provides motivation to move from the current highly centralized control of resources toward a more distributed control structure. This paper presents an agent-based fully distributed dc security constrained optimal power flow (DC-SCOPF) approach. The algorithm is based on a multi-agent system, in which geographically distributed network entities are designated as agents with local sensing, communication, and computation abilities as envisioned for a smart grid. This distributed approach is based on solving the first order optimality conditions associated with DC-SCOPF formulation through an iterative process. At each iteration, each agent updates a few local variables through simple computations, and exchanges information with its neighboring agents. In particular, the updates for each agent incorporate local information such as the Lagrange multipliers, as well as enforcing the supply/demand balance through a local innovation term. Also, the performance is evaluated using the IEEE test systems with 14 and 57 buses and a 944-bus system consisting of eight IEEE 118-bus systems. Finally, analytical arguments concerning the convergence of the proposed method to the optimal solution of the DC-SCOPF are provided.

Hybrid flow shop rescheduling algorithm for perishable products subject to a due date with random invalidity to the operational unit

In China, delivery delays due to a high probability of operational unit failure often occur in perishable manufacturing systems. This paper proposes a rescheduling algorithm known as complete rerouting (CR) for perishable manufacturing systems. The perishable characteristics of agri-products and the stochastic invalidity of operational units are researched. Products and operational units of the system are simulated by agents. In the developed virtual perishable manufacturing system, multiple product agents with different deadlines, values, and due dates search for the best processing route according to scheduling principles. When an operational unit fails stochastically, agents update their status and reroute their path using the rescheduling algorithm. The transition of agents can be described by the Petri net model proposed in this paper. Three basic experiments are investigated, validating the rescheduling algorithm proposed. This study demonstrates that the CR policy causes product agents to search for the best rerouting path compared to other rescheduling policies (i.e., right-shift scheduling and new-job rerouting). The optimal delivery times of products are determined based on penalty cost. Furthermore, this study analyzes the sensitivity of scheduling performance due to different product batches with two failed operational unit statuses (i.e., high frequency, short maintenance, and low frequency, long maintenance).

ARock: An Algorithmic Framework for Asynchronous Parallel Coordinate Updates

Finding a fixed point to a nonexpansive operator, i.e., $x^*=Tx^*$, abstracts many problems in numerical linear algebra, optimization, and other areas of scientific computing. To solve fixed-point problems, we propose ARock, an algorithmic framework in which multiple agents (machines, processors, or cores) update $x$ in an asynchronous parallel fashion. Asynchrony is crucial to parallel computing since it reduces synchronization wait, relaxes communication bottleneck, and thus speeds up computing significantly. At each step of ARock, an agent updates a randomly selected coordinate $x_i$ based on possibly out-of-date information on $x$. The agents share $x$ through either global memory or communication. If writing $x_i$ is atomic, the agents can read and write $x$ without memory locks. Theoretically, we show that if the nonexpansive operator $T$ has a fixed point, then with probability one, ARock generates a sequence that converges to a fixed points of $T$. Our conditions on $T$ and step sizes are weaker than comparable work. Linear convergence is also obtained. We propose special cases of ARock for linear systems, convex optimization, machine learning, as well as distributed and decentralized consensus problems. Numerical experiments of solving sparse logistic regression problems are presented.

Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing

This paper studies the problem of sequential Gaussian shift-in-mean hypothesis testing in a distributed multi-agent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision statistics by simultaneously processing latest observations (innovations) sensed sequentially over time and information obtained from neighboring agents (consensus). For each pre-specified set of type I and type II error probabilities, local decision parameters are derived which ensure that the algorithm achieves the desired error performance and terminates in finite time almost surely (a.s.) at each network agent. Large deviation exponents for the tail probabilities of the agent stopping time distributions are obtained and it is shown that asymptotically (in the number of agents or in the high signal-to-noise-ratio regime) these exponents associated with the distributed algorithm approach that of the optimal centralized detector. The expected stopping time for the proposed algorithm at each network agent is evaluated and is benchmarked with respect to the optimal centralized algorithm. The efficiency of the proposed algorithm in the sense of the expected stopping times is characterized in terms of network connectivity. Finally, simulation studies are presented which illustrate and verify the analytical findings.

Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics

This paper focuses on the problem of recursive nonlinear least squares parameter estimation in multi-agent networks, in which the individual agents observe sequentially over time an independent and identically distributed (i.i.d.) time-series consisting of a nonlinear function of the true but unknown parameter corrupted by noise. A distributed recursive estimator of the \emph{consensus} + \emph{innovations} type, namely $\mathcal{CIWNLS}$, is proposed, in which the agents update their parameter estimates at each observation sampling epoch in a collaborative way by simultaneously processing the latest locally sensed information~(\emph{innovations}) and the parameter estimates from other agents~(\emph{consensus}) in the local neighborhood conforming to a pre-specified inter-agent communication topology. Under rather weak conditions on the connectivity of the inter-agent communication and a \emph{global observability} criterion, it is shown that at every network agent, the proposed algorithm leads to consistent parameter estimates. Furthermore, under standard smoothness assumptions on the local observation functions, the distributed estimator is shown to yield order-optimal convergence rates, i.e., as far as the order of pathwise convergence is concerned, the local parameter estimates at each agent are as good as the optimal centralized nonlinear least squares estimator which would require access to all the observations across all the agents at all times. In order to benchmark the performance of the proposed distributed $\mathcal{CIWNLS}$ estimator with that of the centralized nonlinear least squares estimator, the asymptotic normality of the estimate sequence is established and the asymptotic covariance of the distributed estimator is evaluated. Finally, simulation results are presented which illustrate and verify the analytical findings.

Decentralized Event-Based Communication Strategy on Leader-Follower Consensus Control

This paper addresses the leader-follower consensus problem of networked systems by using a decentralized event-based control strategy. The event-based control strategy makes the controllers of agents update at aperiodic event instants. Two decentralized event functions are designed to generate these event instants. In particular, the second event function only uses its own information and the neighbors’ states at their latest event instants. By using this event function, no continuous communication among followers is required. As the followers only communicate at these discrete event instants, this strategy is able to save communication and to reduce channel occupation. It is analytically shown that the leader-follower networked system is able to reach consensus by utilizing the proposed control strategy. Simulation examples are shown to illustrate effectiveness of the proposed control strategy.

MERGING OF OPINIONS AND PROBABILITY KINEMATICS

AbstractWe explore the question of whether sustained rational disagreement is possible from a broadly Bayesian perspective. The setting is one where agents update on the same information, with special consideration being given to the case of uncertain information. The classical merging of opinions theorem of Blackwell and Dubins shows when updated beliefs come and stay closer for Bayesian conditioning. We extend this result to a type of Jeffrey conditioning where agents update on evidence that is uncertain but solid (hard Jeffrey shifts). However, merging of beliefs does not generally hold for Jeffrey conditioning on evidence that is fluid (soft Jeffrey shifts, Field shifts). Several theorems on the asymptotic behavior of subjective probabilities are proven. Taken together they show that while a consensus nearly always emerges in important special cases, sustained rational disagreement can be expected in many other situations.

Topology optimization of double- and triple-layer grids using a hybrid methodology

In this article, a hybrid methodology combining evolutionary structural optimization (ESO) and gravitational particle swarm (GPS) methods is proposed for topology optimization of double- and triple-layer grids. In the present methodology, which is called the ESO-GPS method, the size optimization of double- and triple-layer grids is first performed by ESO. Then, the outcomes of the ESO are used to improve the GPS through four modifications. Structural weight is minimized against constraints on the displacements of nodes, internal stresses and element slenderness ratio. The GPS is used to investigate the optimum topology of large-scale skeletal structures with discrete variables whose agents update their respective positions by the particle swarm optimization velocity and the acceleration of the gravitational search algorithm. The numerical results show that the proposed algorithm, the ESO-GPS, performs better than the GPS and the other methods presented in the literature.