GLAD: a simple adaptive strategy that yields cooperation in dilemma games

We introduce a simple adaptive rule where agents choose a cooperative effort on a grid. Agents can adjust this effort step by step and G ains and L osses A djust D irections. We show that this process converges to the cooperative outcome in a two-person Prisoners’ Dilemma game, and we provide simulations showing that the results also holds with a larger number of agents.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

Output regulation for linear multi-agent systems with unmeasurable nodes

The output regulation of linear multi-agent systems with partial unmeasurable agents is investigated in this paper. All the agents except the exosystem can be classified into two groups. Agents in the first group can be measured by themselves and their neighbors. State variables are not fully accessible for direct communication and full order Luenberger observers are constructed for the unmeasurable agents. We give a state feedback control law to solve the output regulation problem under the communication topologies based on both measurable and unmeasurable agents. The heterogeneous agents’ synchronization problem is a general case of our results. Finally, examples are utilized to show the effectiveness of the obtained results.     

External activation promoting consensus formation in the opinion model with interest decay

We put forward an opinion model that considers internal decay and external activation or deactivation. Agents may withdraw from the discussion, meanwhile, these inactive agents are likely to be motivated by active neighbors. In addition, external influence from outside circumstances is added to the population. We focus on the majority rule of opinion exchange. Our investigations reveal under the impact of external circumstances, the system evolves to different stable states. One opinion can finally be made dominant when the internal motivation is large sufficiently. However, without external activation, consensus is hardly reached in the system with interest decay.     

Bubbles,crashes and intermittency in agent based market models

We define and study a rather complex market model, inspired from the Santa Fe artificial market and the Minority Game. Agents have different strategies among which they can choose, according to their relative profitability, with the possibility of not participating to the market. The price is updated according to the excess demand, and the wealth of the agents is properly accounted for. Only two parameters play a significant role: one describes the impact of trading on the price, and the other describes the propensity of agents to be trend following or contrarian. We observe three different regimes, depending on the value of these two parameters: an oscillating phase with bubbles and crashes, an intermittent phase and a stable `rational' market phase. The statistics of price changes in the intermittent phase resembles that of real price changes, with small linear correlations, fat tails and long range volatility clustering. We discuss how the time dependence of these two parameters spontaneously drives the system in the intermittent region. We analyze quantitatively the temporal correlation of activity in the intermittent phase, and show that the `random time strategy shift' mechanism that we proposed earlier allows one to understand the observed long ranged correlations. Other mechanisms leading to long ranged correlations are also reviewed. We discuss several other issues, such as the formation of bubbles and crashes, the influence of transaction costs and the distribution of agents wealth. Received 5 July 2002 / Received in final form 9 December 2002 Published online 14 February 2003 RID="a" ID="a"e-mail: irene.giardina@roma1.infn.it     

Food web model with detritus path

We present and study a lattice (Monte Carlo) model of a food web consisting of three levels. Agents on the lowest level produce food from dead agents (detritus) of the upper levels and are themselves eaten by the first level species, which in turn are prey for the top level species. Agents which do not find food in a given time, die with a given probability, while eating enables them to produce offspring in their neighborhood. This rule applies to species on all levels, including the lowest one. The dynamics is therefore nutrient limited. We are considering two pathways — grazers and detritus (using dead organic matter). We show that the emerging dynamics is more complex than the ordinary predator-prey systems in which bottom species are indestructible. We investigate the viability of our model and we construct appropriate (extinct-alive) phase diagrams. We demonstrate how the temporal fluctuations in the densities of the three populations are correlated. We show also that the density of the middle level agents plays the key role in the viability of the investigated food web.     

The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments

We study the lock-in effect in a network of task assignments. Agents have a heterogeneous fitness for solving tasks and can redistribute unfinished tasks to other agents. They learn over time to whom to reassign tasks and preferably choose agents with higher fitness. A lock-in occurs if reassignments can no longer adapt. Agents overwhelmed with tasks then fail, leading to failure cascades. We find that the probability for lock-ins and systemic failures increase with the heterogeneity in fitness values. To study this dependence, we use the Shannon entropy of the network of task assignments. A detailed discussion links our findings to the problem of resilience and observations in social systems.     

From market games to real-world markets

This paper uses the development of multi-agent market models to present a unified approach to the joint questions of how financial market movements may be simulated, predicted, and hedged against. We first present the results of agent-based market simulations in which traders equipped with simple buy/sell strategies and limited information compete in speculatory trading. We examine the effect of different market clearing mechanisms and show that implementation of a simple Walrasian auction leads to unstable market dynamics. We then show that a more realistic out-of-equilibrium clearing process leads to dynamics that closely resemble real financial movements, with fat-tailed price increments, clustered volatility and high volume autocorrelation. We then show that replacing the `synthetic' price history used by these simulations with data taken from real financial time-series leads to the remarkable result that the agents can collectively learn to identify moments in the market where profit is attainable. Hence on real financial data, the system as a whole can perform better than random. We then employ the formalism of Bouchaud in conjunction with agent based models to show that in general risk cannot be eliminated from trading with these models. We also show that, in the presence of transaction costs, the risk of option writing is greatly increased. This risk, and the costs, can however be reduced through the use of a delta-hedging strategy with modified, time-dependent volatility structure. Received 30 August 2000     

An agent-based model of collective emotions in online communities

We develop an agent-based framework to model the emergence of collective emotions, which is applied to online communities. Agent’s individual emotions are described by their valence and arousal. Using the concept of Brownian agents, these variables change according to a stochastic dynamics, which also considers the feedback from online communication. Agents generate emotional information, which is stored and distributed in a field modeling the online medium. This field affects the emotional states of agents in a non-linear manner. We derive conditions for the emergence of collective emotions, observable in a bimodal valence distribution. Dependent on a saturated or a superlinear feedback between the information field and the agent’s arousal, we further identify scenarios where collective emotions only appear once or in a repeated manner. The analytical results are illustrated by agent-based computer simulations. Our framework provides testable hypotheses about the emergence of collective emotions, which can be verified by data from online communities.     

Fast and slow light generated simultaneously for up-down-link frequency converter applications

We firstly propose a new system for simultaneous fast and slow light generation using a soliton pulse propagating within the nonlinear micro-ring resonators. The nonlinear Kerr effect induces the spreading frequency bands within the micro-ring device, where the chaotic filtering characteristics can be employed using the appropriate micro-ring parameters. Results obtained have shown that the wide spreading of frequency bands can be generated and selected to form the optical wireless communication links. In this work, the selected down-link and up-link frequency bands are 500 MHz and 2 GHz, respectively. The proposed system can be implemented within the mobile telephone hand set, where the two different frequency carriers within the same frequency bands can be selected to form the up-down-link converters, which means that the frequency converter can be performed within a single system.     

The Consensus Problem in Polities of Agents with Dissimilar Cognitive Architectures

Agents interacting with their environments, machine or otherwise, arrive at decisions based on their incomplete access to data and their particular cognitive architecture, including data sampling frequency and memory storage limitations. In particular, the same data streams, sampled and stored differently, may cause agents to arrive at different conclusions and to take different actions. This phenomenon has a drastic impact on polities—populations of agents predicated on the sharing of information. We show that, even under ideal conditions, polities consisting of epistemic agents with heterogeneous cognitive architectures might not achieve consensus concerning what conclusions to draw from datastreams. Transfer entropy applied to a toy model of a polity is analyzed to showcase this effect when the dynamics of the environment is known. As an illustration where the dynamics is not known, we examine empirical data streams relevant to climate and show the consensus problem manifest.     

Rule-based dynamic TV white space spectrum sharing services composition framework

When developing a TV White Space (TVWS) system with the available TV spectrum after digital switchover, the smooth introduction of new technologies and components has to be supported because the requirements of users change frequently in a new system. We need to provide a modular, extensible and easy to implement framework rather than building highly integrated systems. According to environment, rules, polices and users’ requirements, advanced intelligent management functionalities that can guarantee the proper configuration of devices in TVWS networks and proper load balance, have to be developed. According to different requests from users, different algorithms and components have to be used to meet the needs of the users. How, when and which algorithms and components should be combined in order to meet the requests from the users become essential problems. Based on Service Oriented Architecture, all things in the proposed system including algorithms and components can be defined as services. This paper presents a framework of rule-based dynamic TVWS services composition that can meet the above needs and combine different TVWS services to fulfil users’ requirements dynamically.     

Exact solution of a modified El Farol's bar problem: Efficiency and the role of market impact

We discuss a model of heterogeneous, inductive rational agents inspired by the El Farol Bar problem and the Minority Game. As in markets, agents interact through a collective aggregate variable — which plays a role similar to price — whose value is fixed by all of them. Agents follow a simple reinforcement-learning dynamics where the reinforcement, for each of their available strategies, is related to the payoff delivered by that strategy. We derive the exact solution of the model in the “thermodynamic” limit of infinitely many agents using tools of statistical physics of disordered systems. Our results show that the impact of agents on the market price plays a key role: even though price has a weak dependence on the behavior of each individual agent, the collective behavior crucially depends on whether agents account for such dependence or not. Remarkably, if the adaptive behavior of agents accounts even “infinitesimally” for this dependence they can, in a whole range of parameters, reduce global fluctuations by a finite amount. Both global efficiency and individual utility improve with respect to a “price taker” behavior if agents account for their market impact.     

How do ensembles occupy space?

To find an answer to the title question, an attractiveness function between agents and locations is introduced yielding a phenomenological but generic model for the search for optimal distributions of agents over space. Agents can be seen as, e.g., members of biological populations like colonies of bacteria, swarms, and so on. The global attractiveness between agents and locations is maximized causing (self-propelled) `motion' of agents and, eventually, distinct distributions of agents over space. At the same token spontaneous changes or `decisions' are realized via competitions between agents as well as between locations. Hence, the model's solutions can be considered a sequence of decisions of agents during their search for a proper location. Depending on initial conditions both optimal as well as suboptimal configurations can be reached. For the latter early decision-making are important for avoiding possible conflicts: if the proper moment is missed, then only a few agents can find an optimal solution. Indeed, there is a delicate interplay between the values of the attractiveness function and the constraints as can be expressed by distinct terms of a potential function containing different Lagrange parameters. The model should be viewed as a top-down approach as it describes the dynamics of order parameters, i.e. macroscopic variables that reflect affiliations between agents and locations. The dynamics, however, is modified via so-called cost functions that are interpreted in terms of affinity levels. This interpretation can be seen as an original step towards an understanding of the dynamics at the underlying microscopic level. When focusing on the agent, one may say that the dynamics of an order parameter shows the evolution of an agent's intrinsic `map' for solving the problem of space occupation. Importantly, the dynamics does not necessarily distinguish between evolving (or moving) agents and evolving (or moving) locations though agents are more likely to be actors than the locations. Put differently, an order parameter describes an internal map which is linked to the expectation of an agent to find a certain location. Owing to the dynamical representation, we can therefore follow up the change of these maps over time leading from uncertainty to certainty.     

Optical Devices for Optical Access Networks Recent Progress in Semiconductor Optical Devices and Optical Modules

Cost-effectiveness is essential in developing optical access network systems. To reduce system costs, both improved system and component technologies are required. Reducing the costs of optical devices and modules in an optical network unit is especially necessary. In this paper, the requirements for optical devices in optical access networks and modules are clarified. Moreover, we also review the recent progress in technologies for semiconductor optical devices and hybrid integration for low-cost optical modules in access networks.     

夜视领域几个热点技术的进展及分析

作为夜视技术的主要技术途径,微光夜视和红外热成像技术在现代战争中具有重要的地位,掌握先进的夜视技术对于控制战场形势至关重要。随着各种现代夜视技术在世界各国军队中的装备中应用,"拥有黑夜"已逐步成为"共享黑夜"。为继续保持夜视技术的优势,美国及其盟友不断地发展更为先进的夜视技术和装备,并取得重要进展。介绍了夜视技术的几个重要的技术进展,分析了其对整个夜视技术发展以及应用的影响,希望对加快发展我国的夜视技术,促进夜视装备的有效应用起到促进作用。     

The role of technology in the past and future development of the doubly labelled water method

The doubly labelled water method is an isotope-based technique that is used to measure the energy demands of free-living animals and humans. It is based on the observation that, in the body, the oxygen in carbon dioxide is in complete isotope exchange equilibrium with the oxygen in body water. Hence, a label of isotopic oxygen in body water is eliminated by both respiratory CO(2) and water turnover, whereas a similarly introduced label of deuterium is eliminated only by water flux. The difference in isotope fluxes therefore permits estimation of CO(2) production, which is correlated to energy demands. The doubly labelled water method has been advanced predominantly by technological advances in mass spectrometry. Although it was first described in the 1950s, it was only used on small animals and in low numbers because the costs of the isotopes were a primary constraint. However, advances in mass spectrometry precision and accuracy in the 1980s made it possible to reduce the quantities of isotope used, and hence apply the method on humans, although still in small numbers. The advent of continuous flow inlets in the 1990s made possible the processing of samples in much larger numbers and the sample sizes of studies have expanded. Ironically, however, the technique is now under treat because of technological advances in another area (positron emission tomography), which has generated an enormous demand for (18)O and pushed up the price of isotopes. A continuation of this trend might drive prices to levels where sustained application of the method in human studies is questionable. Replacing determination of isotope enrichments currently performed by isotope ratio mass spectrometry with determinations made by stable isotope infrared laser spectrometry may be a technological advance that will get us out of this problem.     

Modeling and simulation of a double auction artificial financial market

We present a double-auction artificial financial market populated by heterogeneous agents who trade one risky asset in exchange for cash. Agents issue random orders subject to budget constraints. The limit prices of orders may depend on past market volatility. Limit orders are stored in the book whereas market orders give immediate birth to transactions. We show that fat tails and volatility clustering are recovered by means of very simple assumptions. We also investigate two important stylized facts of the limit order book, i.e., the distribution of waiting times between two consecutive transactions and the instantaneous price impact function. We show both theoretically and through simulations that if the order waiting times are exponentially distributed, even trading waiting times are also exponentially distributed.     

Models of collective cell motion for cell populations with different aspect ratio: Diffusion,proliferation and travelling waves

Continuum, partial differential equation models are often used to describe the collective motion of cell populations, with various types of motility represented by the choice of diffusion coefficient, and cell proliferation captured by the source terms. Previously, the choice of diffusion coefficient has been largely arbitrary, with the decision to choose a particular linear or nonlinear form generally based on calibration arguments rather than making any physical connection with the underlying individual-level properties of the cell motility mechanism. In this work we provide a new link between individual-level models, which account for important cell properties such as varying cell shape and volume exclusion, and population-level partial differential equation models. We work in an exclusion process framework, considering aligned, elongated cells that may occupy more than one lattice site, in order to represent populations of agents with different sizes. Three different idealisations of the individual-level mechanism are proposed, and these are connected to three different partial differential equations, each with a different diffusion coefficient; one linear, one nonlinear and degenerate and one nonlinear and nondegenerate. We test the ability of these three models to predict the population-level response of a cell spreading problem for both proliferative and nonproliferative cases. We also explore the potential of our models to predict long time travelling wave invasion rates and extend our results to two-dimensional spreading and invasion. Our results show that each model can accurately predict density data for nonproliferative systems, but that only one does so for proliferative systems. Hence great care must be taken to predict density data with varying cell shape.