Interaction Of Agents Research Articles

Pedestrian Trajectory Prediction for Real-Time Autonomous Systems via Context-Augmented Transformer Networks.

Forecasting the trajectory of pedestrians in shared urban traffic environments from non-invasive sensor modalities is still considered one of the challenging problems facing the development of autonomous vehicles (AVs). In the literature, this problem is often tackled using recurrent neural networks (RNNs). Despite the powerful capabilities of RNNs in capturing the temporal dependency in the pedestrians’ motion trajectories, they were argued to be challenged when dealing with longer sequential data. Additionally, whilst the accommodation for contextual information (such as scene semantics and agents interactions) was shown to be effective for robust trajectory prediction, they can also impact the overall real-time performance of prediction system. Thus, in this work, we are introducing a framework based on the transformer networks that were demonstrated recently to be more efficient and outperformed RNNs in many sequential-based tasks. We relied on a fusion of sensor modalities, namely the past positional information, agent interactions information and scene physical semantics information as an input to our framework in order to not only provide a robust trajectory prediction of pedestrians, but also achieve real-time performance for multi-pedestrians’ trajectory prediction. We have evaluated our framework on three real-life datasets of pedestrians in shared urban traffic environments and it has outperformed the compared baseline approaches in both short-term and long-term prediction horizons. For the short-term prediction horizon, our approach has achieved lower scores according to the average displacement error and the root-mean squared error (ADE/RMSE) of predictions over the state-of-the art (SOTA) approach by more than 11 cm and 23 cm, respectively. While for the long-term prediction horizon, our approach has achieved lower ADE and FDE over the SOTA approach by more than 62 cm and 165 cm, respectively. Additionally, our approach has achieved superior real time performance by scoring only 0.025 s (i.e., it can provide 40 individual trajectory predictions per second).

AN ALGORITHM OF THE COLLECTIVE INTERACTION OF INTELLIGENT AGENTS IN CENTRALIZED MULTI-AGENT SYSTEMS

A centralized multiagent system based on the methods of feudal reinforcement learning, including agents-managers and agents-subordinates, is considered. A brief review of the author’s previous works on this topic is given. The standard algorithm for the functioning of systems of this type is considered, including the translation of the decision maker to agents-managers, the division of tasks by agents-managers into a set of subtasks, the choice by the agent-manager of the strategy used, the formation of reward functions by agents-managers, the assignment of tasks to agents-subordinates, the execution by agents-subordinates assigned tasks. The main problems of this algorithm are presented, changes are made to ensure the possibility of automatically assigning agent-managers and forming groups of subordinate agents around them, reproducing and exchanging experience. More attention is paid to the problem of experience exchange, the main ways of experience exchange are given. The principles of operation of a machine vision system that implements an upgraded algorithm are described. An assessment of the effectiveness of the obtained algorithm for the collective interaction of intelligent agents using a software model developed in Microsoft Unity is given. A comparison is made between the standard algorithm for multiagent interaction and the proposed algorithm for the collective interaction of intelligent agents in centralized multi-agent systems based on the approach of reinforcement learning and visualization of three-dimensional scenes. The conclusion is made about the expediency of using the developed algorithmt.

Characterization of the Interaction of Nerve Agent Mimics with Selected Synthetic Receptors

Qualitative NMR spectroscopic and quantitative calorimetric binding studies were performed to characterize the interaction of nontoxic mimics of the V-type nerve agent VX (O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate) and the Novichok nerve agent A-234 (ethyl (1-(diethylamino)ethylidene)phosphoramidofluoridate) with a series of receptors in 100 mM aqueous phosphate buffer at pH 7.4 and 37 °C. These investigations provided information about the preferred geometry with which the nerve agent mimics are included into the receptor cavities and about the stability of the complexes formed. According to the results, the positively charged VX mimic prefers to bind to cation receptors such as sulfonated calixarenes and an acyclic cucurbituril but does not noticeably interact with cyclodextrins. While binding to the acyclic cucurbituril is stronger than that to calixarenes, the mode of inclusion into the sulfonatocalix[4]arene cavity is better suited for the development of scavengers that bind and detoxify V-type nerve agents. The neutral Novichok mimic, on the other hand, only interacts with the acyclic cucurbituril with a strength required for scavenger development. These binding studies thus provided guidelines for the further development of nerve agent scavengers.

Public administration and political science: From monogamous marriage to professional partnership

Public administration and political science: From monogamous marriage to professional partnership

Bio-graphene production from oil palm shell waste valorised through sequential thermal and catalytic means

Cellulose, hemicellulose and lignin made up a large majority of biomass in the world. These three organic polymers can produce bio-gas such as methane (CH4) and carbon dioxide (CO2) which are necessary for bio-graphene fabrication. Carbon layer deposition depends on CH4 while the uniformity and porosity of the carbon layer depend on CO2 and activation agent interaction. By altering the Oil Palm Shell (OPS) biomass precursor through pyrolysis, the remaining content of biomass precursors will enhance bio-graphene development through Chemical Vapor Deposition (CVD) process. The alteration of these biomass precursors is made through thermal manipulation at temperatures of 200 ∘C, 400 ∘C and 600 ∘C. The best thermal means will produce bio-graphene encompassing observable crystallinity, appropriate exfoliation, low defect, high carbon and low oxygen value which possess characteristics that are similar to bio-graphene fabricated from the past years. The different pre-treated biomass through pyrolysis are characterized using proximate analysis, Fourier-transform infrared spectroscopy (FTIR), Field-Emission Scanning Electron Microscope (FESEM), Energy dispersive X-ray (EDX), X-ray diffraction (XRD) and Micro-Raman. Additionally, the different bio-graphene samples fabricated from pre-treated biomass precursors were identified and characterized using Atomic Force Microscopy (AFM), Ultra-high Resolution Scanning Electron Microscope (U-SEM), Energy dispersive X-ray (EDX), X-ray diffraction (XRD) and Micro-Raman spectrometer.

Paclitaxel-conjugated phenylboronic acid-enriched catalytic robots as smart drug delivery systems

Artificial motors (robots) that are inspired by natural motors have captivated much attention with their attractive performances and special features. These miniaturized nano/micromotors have succeeded many important tasks in different fields including (bio)sensing, cargo transport, cell isolation and trapping, nanosurgery, gene therapy, and drug delivery. Herein, paclitaxel (PTX)-conjugated robots consisted of poly(3-aminophenylboronic acid) outer layer, a platinum (Pt)-nickel (Ni) segment, and a Pt catalytic inner layer were constructed to demonstrate their effective use for smart drug delivery. These self-functionalized motors had effective and smooth autonomous movement at velocities of 44 ± 9 μm/s and 34 ± 4 μm/s after 6 and 12 h of drug incubation steps. Fabrication of these motors was relied on electrochemical protocols, and the drug loading process was carried out by the interaction of the antitumor agent with the outer polymeric layer of the robots. Targeted delivery of the drug was achieved by the controlled driven of the robots toward human breast cancer cell line via induced Near-Infrared irradiation (NIR). pH dependency of the system was also demonstrated at acidic pH values. It was shown that the near-infrared irradiation increased the efficiency of the drug release through the polymeric robots. Polymeric robots showed promising viability results by using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide tetrazolium reduction (MTT) assays in order to signify potential and practical applications.

EFFICIENT FUNCTIONING OF DIGITAL INNOVATIVE TECHNOLOGICAL PLATFORMS: INSTITUTIONAL DIMENSION

The rapid development of complex digitalization processes, the formation and development of digital platforms is significantly ahead of the possibilities of their full-fledged, comprehensive, interdisciplinary understanding, both in theoretical and methodological, and organizational, managerial, and methodological aspects. The author of the article implements the target setting to fill the existing gaps in the study of platform-network interaction of agents of digital innovative technological platforms with an emphasis on its possible measurements and assessment. There is a certain difficulty in solving this popular problem, given the predominance of a formal-organizational approach in the corresponding evaluation system (moreover, for certain types of platforms). We have carried out a certain localization of the subject of study — the measurement of key parameters of platform-network interaction in the institutional aspect. A conceptual and institutional model for measuring the key qualitative parameters of the effective functioning of digital innovative technological platforms is proposed based on the synthesis of the provisions of the institutional theory and the theory of complex systems management, as well as the development and use of a system of organizational and economic criteria. Within the framework of the proposed conceptual and institutional model, an organizational and managerial development of measuring key parameters (organizational, informational, intellectual-technological, socio-economic) was carried out on the basis of tools: theoretical (information-network theory) and practical (the demanded model "structure-behavior-result", in the author’s interpretation). The system-forming factor of ensuring the fine tuning of a complex, often contradictory, multi-agent process of the effective functioning of innovative technological platforms is singled out. This is an intellectual and competence factor represented by highly qualified developers, managers, and experts with specialized interdisciplinary training. These author’s developments can find practical application as theoretical and methodological foundations for ensuring the productivity of business strategies and business models of Russian high-tech innovative companies — agents of digital platforms, including international ones. The developments are designed to reduce the level of uncertainty and risks that impede, as evidenced by foreign experience, the effective functioning of platforms and often lead to their destruction.

What is new with Artificial Intelligence? Human-agent interactions through the lens of social agency.

In this article, we suggest that the study of social interactions and the development of a “sense of agency” in joint action can help determine the content of relevant explanations to be implemented in artificial systems to make them “explainable.” The introduction of automated systems, and more broadly of Artificial Intelligence (AI), into many domains has profoundly changed the nature of human activity, as well as the subjective experience that agents have of their own actions and their consequences – an experience that is commonly referred to as sense of agency. We propose to examine the empirical evidence supporting this impact of automation on individuals’ sense of agency, and hence on measures as diverse as operator performance, system explicability and acceptability. Because of some of its key characteristics, AI occupies a special status in the artificial systems landscape. We suggest that this status prompts us to reconsider human–AI interactions in the light of human–human relations. We approach the study of joint actions in human social interactions to deduce what key features are necessary for the development of a reliable sense of agency in a social context and suggest that such framework can help define what constitutes a good explanation. Finally, we propose possible directions to improve human–AI interactions and, in particular, to restore the sense of agency of human operators, improve their confidence in the decisions made by artificial agents, and increase the acceptability of such agents.

71 Agent-Based Modeling: A Historical Perspective and Comparison to Other Modeling Techniques

Abstract The earliest known history of agent-based modeling (ABM) is traced back to Von Neumann’s self-reproducing cellular automata, designed in the 1940s. The usage of ABM in different scientific fields accelerated in the 1990s, supported by the phenomenal increase in computing capabilities. ABM is gaining acceptance in animal systems, as daily decision-making is becoming complex due to multiple competing outcomes of interest to food systems stakeholders. ABM typically replicates complex real-world systems, for example, a herd of animals that dynamically interact based on simple rules. ABM simulate the heterogenous, stochastic characteristics of agents observed in the real-world. The dynamic interaction of agents replicates the observable real-world complex system patterns. Application of ABM in animal systems ranges from modeling cell behavior, precision nutrition and herd management to predicting the spread of epidemics, and food animal supply chain optimization. Animal science's most widely used alternative modeling techniques include system dynamic models, differential equations-based models, and statistical modeling. The list of unrealistic assumptions that limit the utility of these modeling techniques includes the assumptions of linearity, homogeneity, normality, and stationarity. The advantageous characteristics of an agent in ABM that set it apart from other techniques include being identifiable, capable of existing in an environment where it interacts with other agents while being autonomous and self-directed, goal-oriented behavior, flexible learning, and capability of adaptations in its behavior over time, based on experience. Identifying the purpose of the model, the questions the model will answer, and the potential users are the key decision variables modelers should ponder upon before embarking on building ABM. The commonly used ABM software includes Repast, Swarm, Netlogo, and MASON. Developing an ABM has several highly interleaved stages: concept development, requirements definition, design, implementation, and operationalization, each of which will be illustrated during the hands-on training session on ABM.

The analysis of space category in economic studies

Space is one of the key and controversial (in terms of content) categories in economics. It is assumed that the methodological basis of spatial development concept, and, correspondingly, the very concept of space itself are classical (mercantilism) and neoclassical (price theory) theories. However, until now the understanding of its nature and its role in creating and transforming modern socio-economic relations remains a debatable issue. The key method of analysis in this work is semantic analysis. The Authors attempt, on the one hand, to reveal a retrospective transformation of space concept, and on the other, to identify the key theoretical and methodological approaches to this phenomenon in modern research. The analysis results in a number of valuable findings. First, the authors identify a synthetic nature of concept evolution, which consists in a specific absorption of previously proposed models and space describing provisions. Secondly, the authors identify the main approaches to space study, for which the determining criterion is the nature of socio-economic relations formed by economic agents, and the processes that these agents implement in various spaces. Thirdly, the authors prove that modern concept of socio-economic space allows for a broader description of interactions of economic agents in translocation aspect. Thus, the scientific significance of the research consists in expanding knowledge approaches to defining the category of space, as well as its analysis in modern economic research.

Multi-agent Intelligent Ensembles with Professional Competencies, Pattern Recognition and Decision Making

Each competence is exercised by an intellectual agent with a competent functional professional image. Intellectual agents form an ensemble with clever ethical artificial intelligence. The use of an ensemble with intelligent ethical artificial intelligence in various environments is carried out by synergistically adjusting the interaction of intelligent agents based on data from a specific environment obtained by an analytical competent intellectual agent. Modeling holographic processes of the human psyche based on artificial intelligence of machine learning with Fourier transformation using full parametric sequences of necessary and sufficient data of holograms of target objects solves the problem of their unambiguous detection in different environments and in different conditions. An ensemble of intelligent decision-making agents is a cognitive information system that makes a decision based on an objective analysis of available data in difficult situations, in an interactive mode, taking into account performance criteria and resource-time constraints. Decision-making criteria are functionalities that express preferences and allow ranking the quality of decisions. Decisions are made on the basis of rules. Decision rules are a set of logical constructs used to produce a decision based on criteria, data, and knowledge. An ensemble of intellectual agents with professional competencies, pattern recognition and decision-making fully model the abilities of the human psyche.

Diversifying agent's behaviors in interactive decision models

Modeling other agents' behaviors plays an important role in decision models for interactions among multiple agents. To optimize its own decisions, a subject agent needs to model what other agents act simultaneously in an uncertain environment. However, modeling insufficiency occurs when the agents are competitive and the subject agent cannot get full knowledge about other agents. Even when the agents are collaborative, they may not share their true behaviors due to their privacy concerns. Most of the recent research still assumes that the agents have common knowledge about their environments and a subject agent has the true behavior of other agents in its mind. Consequently, the resulting techniques are not applicable in many practical problem domains. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model before their interactions. The challenges lie in generating and measuring new behaviors of other agents. Starting with prior knowledge about other agents' behaviors, we use a linear reduction technique to extract representative behavioral features from the known behaviors. We subsequently generate their new behaviors by expanding the features and propose two diversity measurements to select top- K $K$ behaviors. We demonstrate the performance of the new techniques in two well-studied problem domains. The top- K $K$ behavior selection embarks the study of unknown behaviors in multiagent decision making and inspires investigation of diversifying agents' behaviors in competitive agent interactions. This study will contribute to intelligent systems dealing with unknown unknowns in an open artificial intelligence world.

Marine Internet of Things Platforms for Interoperability of Marine Robotic Agents: An Overview of Concepts and Architectures

The creation of a Marine Internet of Things platform, including the Underwater Internet of Things, is needed to ensure the interaction and digital navigation of heterogeneous marine robotic agents. It is necessary to combine the following robotic agents: autonomous underwater vehicles, remotely operated vehicles, active and passive marine sensors, buoys, underwater sonar stations, coastal communication posts, and other elements of the platform. To ensure the interaction of all these elements, it is necessary to use a common communication system within the platform, as well as a common navigation and control system to solve complex problems of the navigation and control of the movement of robotic agents in order to implement a joint mission to collect and transmit data, including video information in real time. The architecture of the Marine Internet of Things platform must first be defined in order to use a unified approach to data exchange. This article provides an overview of approaches to determining the architectures of network underwater and marine communication systems based on the concept of the Internet of Things. This paper provides a comprehensive study of MIoT applications, challenges, and architectures. The main contributions of this paper are summarized as follows: we introduce potential MIoT applications; we point out the challenges of MIoT (i.e., the differences between MIoT and IoT); and we analyze the MIoT system architecture.

Intellectual Agent Ensemble with Professional Competencies, Pattern Recognition and Decision Making

Each competence is exercised by an intellectual agent with a competent functional professional image. Intellectual agents form an ensemble with clever ethical artificial intelligence. The use of an ensemble with intelligent ethical artificial intelligence in various environments is carried out by synergistically adjusting the interaction of intelligent agents based on data from a specific environment obtained by an analytical competent intellectual agent. Modeling holographic processes of the human psyche based on artificial intelligence of machine learning with Fourier transformation using full parametric sequences of necessary and sufficient data of holograms of target objects solves the problem of their unambiguous detection in different environments and in different conditions. An ensemble of intelligent decision-making agents is a cognitive information system that makes a decision based on an objective analysis of available data in difficult situations, in an interactive mode, taking into account performance criteria and resource-time constraints. Decision-making criteria are functionalities that express preferences and allow ranking the quality of decisions. Decisions are made on the basis of rules. Decision rules are a set of logical constructs used to produce a decision based on criteria, data, and knowledge. An ensemble of intellectual agents with professional competencies, pattern recognition and decision-making fully model the abilities of the human psyche.

Ecosystem approach to the management of economic agents’ interaction in the industry

The article is devoted to the theoretical and methodological substantiation of the ecosystem theory application to manage the interaction of digital economy actors in the context of industry’s development, including its high-tech sector. The subject of the study was the assessment of objective and subjective components of the process of forming a united digital space necessary to ensure interaction in the enterprise management in the new digital economy, and the goal was to solve the question of choosing a theoretical basis in favor of ecosystem theory. The results of the article carried out using the methods of description, scientific analysis and synthesis consist in identifying the completion of digital transformation when the ecosystem level is reached, substantiating the necessary conditions for this; systematizing the objective need to update classical approaches, proving the need to adapt life cycle models for ecosystems taking into account the uncertainty of technological development and proposing an original cyclic model that takes into account transformation of enterprises and the formats of their interactions in the digital economy. As conclusions, the basic provisions on the elements of a united digital space (platform, network effects and market expectations) and a dynamic model of its formation based on an ecosystem approach are presented. The authors have formed recommendations for the creation of a management system for the interaction of economic agents, taking into account the rules of communication of ecosystem partners, competition between them and the possibilities of coordination. The relevance of the work done is determined by the proposal of a unified interpretation of the process of forming a united digital space as one of the most important consequences of the digital breakthrough of the economy.

Intraguild Interactions of Three Biological Control Agents of the Fall Armyworm Spodoptera frugiperda (JE Smith) in Florida.

Simple SummarySpodoptera frugiperda is a native noctuid pest of the Western Hemisphere with a growing global distribution. It is polyphagous by nature and is the primary insect pest attacking maize in Florida. Larval feeding damage has resulted in crop yield losses of up to 20% or more in the United States. In other parts of the world, it causes maize yield losses of between 17–72%. Consequently, regular pesticide applications have led to insecticide-resistant strains of S. frugiperda. Herein, we propose using biological control agents as an alternative pest management strategy. Augmentative biological control allows for the strategic release of these agents where they are sparse or absent in open fields. However, insects are complex organisms with an array of intraguild interactions that might contradict such an effort. Therefore, the goal of improving these agents’ use for alternative pest control requires evaluating their interactions as expressed in the same guild.The fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is a maize pest worldwide. Its host range comprises more than 350 reported plant species, and it is the primary insect pest attacking maize in Florida. Global trade has not only assisted but accelerated its invasion into the Eastern Hemisphere. Regular pesticide use promotes resistance in the species; therefore, there is an urgent need for alternative pest management strategies. This study evaluated the interactions of biological control agents within a similar guild. Some of the reported interactions could potentially lead to the integration of these agents within the same niche to increase biological control efficiency against the fall armyworm. We evaluated three biocontrol agents that are natural enemies of Lepidopteran pests, the true bugs Podisus maculiventris and Euthyrhynchus floridanus (Hemiptera: Pentatomidae) and a parasitoid, Cotesia marginiventris (Hymenoptera: Braconidae). Depending on their intraguild interactions, these agents could potentially be useful for biological control of the fall armyworm. The study investigated these three biocontrol agents and concluded that integrating these agents to control the fall armyworm is a possibility; however, only under certain conditions. Investigations were focused on evaluating the predator–parasitoid and devised pairing interactions. Predator response to prey in a choice or no-choice scenario and choices based on olfaction or other bodily cues were studied under experimental laboratory conditions.

Моделирование взаимодействия интеллектуальных агентов в мультиагентной системе управления городским парковочным пространством

The possibilities of a multi-agent approach for managing urban parking space are considered, which allows you to adequately represent the parking space and effectively solve the following tasks: monitoring congestion, searching and booking available parking spaces; building routes and navigation to selected places; parking; payment for parking services; monitoring compliance with parking rules; control and access control in closed parking lots (equipped with entrance and exit terminals and barriers); forecasting the main parameters, such as workload, income, turnover; informing users. The necessity of intellectualization of urban parking space management processes based on the use of methods and technologies of multi- agent systems (MAS), the main objectives of which are to: reduce the search time for parking spaces; increase the speed of traffic in paid parking areas; increase the turnover of parking spaces; reduce traffic congestion, fuel costs; reduce the number of parking violations on the road network; reducing the flow of personal vehicles entering the toll zone and stimulating the use of urban public transport; reducing environmental pollution. The greatest difficulty is the tasks of organizing the interaction of agents of various typologies in the collective solution of tasks, since each agent solving a specific task has only a partial idea of the overall task and must constantly interact with other agents. The features of prototyping MAS with an emphasis on modeling the interaction of certain types of intelligent agents in the problem area under study are presented. The obtained simulation results are the basis for the continuation and further development of research and development to create the final prototype of a MAS for urban parking space management.

Феномен переговорной силы на конкурентных товарных рынках

The emergence of network retailers and corporate associative structures in some industries, which became the largest buyers or initiators of market processes, began to form the buyer’s market power or bargaining power of the buyer that became the object of a broader study and confirmed its relevance. The realization of bargaining power on the part of the consumer does not always manifest itself in the system of economic interactions as an action that violates competition, but often is identified as a mechanism of business strategies or a form of implementation of marketing competitive practices. There are types of markets and market situations in which bargaining power is essentially the central link of the transaction, regulating prices, volumes, contract terms and distribution of benefits. The purpose of the study is to consider the phenomenon of bargaining power, since the consequences of its manifestations form long-term losses for both the seller and the market as a whole. To achieve this goal, it is necessary to solve several tasks: to conduct a theoretical analysis of the concept of “bargaining power”, to determine the difference between bargaining power and market power and to identify factors affecting the formation and development of bargaining power. This article focuses on the study of the evolution of theoretical thought in the field of disclosure of the phenomenon of bargaining power. Foreign sources give some understanding of the subject of bargaining power, but the question of factors, research schools and evaluation indicators still remains open. Based on the available bibliographic content, the article presents a comprehensive analysis of the category of “bargaining power” in various conditions of interaction of agents, the established scientific directions of its study. The systematized factors influencing the negotiating power and the vectors of their impact reflect the accumulated author’s opinion of different research views. The article reflects the criteria characteristics of the manifestation of bargaining power in the market. Understanding the essence of the phenomenon under study can be useful for expanding the field of market analysis in cases of antimonopoly proceedings, preventing anticompetitive actions, forming an antimonopoly compliance system and preventing antimonopoly risks.

Electrophysiological Features to Aid in the Construction of Predictive Models of Human-Agent Collaboration in Smart Environments.

Achieving successful human–agent collaboration in the context of smart environments requires the modeling of human behavior for predicting people’s decisions. The goal of the current study was to utilize the TBR and the Alpha band as electrophysiological features that will discriminate between different tasks, each associated with a different depth of reasoning. To that end, we monitored the modulations of the TBR and Alpha, while participants were engaged in performing two cognitive tasks: picking and coordination. In the picking condition (low depth of processing), participants were requested to freely choose a single word out of a string of four words. In the coordination condition (high depth of processing), participants were asked to try and select the same word as an unknown partner that was assigned to them. We performed two types of analyses, one that considers the time factor (i.e., observing dynamic changes across trials) and the other that does not. When the temporal factor was not considered, only Beta was sensitive to the difference between picking and coordination. However, when the temporal factor was included, a transition occurred between cognitive effort and fatigue in the middle stage of the experiment. These results highlight the importance of monitoring the electrophysiological indices, as different factors such as fatigue might affect the instantaneous relative weight of intuitive and deliberate modes of reasoning. Thus, monitoring the response of the human–agent across time in human–agent interactions might turn out to be crucial for smooth coordination in the context of human–computer interaction.

Agents interaction and price dynamics: evidence from the laboratory

Agents interaction and price dynamics: evidence from the laboratory