Behavior Of Agents Research Articles

The Relationship Between Chinese High School English Learners’ Classroom Engagement and English Academic Achievement: A Case Study of English Learners in a High School in Henan China

Classroom engagement is often referred to as a crucial factor for students’ academic achievement. However, within the realm of second language acquisition, only a limited number of scholars made empirical investigations into the association between engagement in the English classroom and English academic achievement. In particular, research on second language learners at the basic education stage is lacking. Therefore, this study examines the current state of English classroom engagement among high school students and its relationship with English academic achievement through a case study of students in Henan China. This study found that high school students’ overall English classroom engagement was high, but individual-based agentic behavior engagement was low. Furthermore, male students exhibited significantly lower levels of various dimensions of English classroom engagement compared to female students. Moreover, interaction-based emotional engagement, individual-based cognitive engagement, individual-based behavioral engagement, and interaction-based conventional behavioral engagement were found to significantly predict students’ English academic achievement. Furthermore, gender is found to exert a significant moderating influence on the association between individual-based cognitive engagement and English academic achievement among students. Implications of these findings for English instruction are discussed.

An uncertainty quantification framework for agent-based modeling and simulation in networked anagram games

ABSTRACT In a networked anagram game, players are provided letters with possible actions of requesting letters from their neighbours, replying to letter requests, or forming words. The objective is to form as many words as possible as a team. The experimental data show that behaviours among players can vary significantly. However, simulations using agent-based models (ABM) in the literature often have not incorporated proper uncertainty quantification methods to characterise diverse behaviours of players. In this work, we propose an uncertainty quantification framework to build, exercise, and evaluate agent behaviour models and simulations for networked group anagram games. Specifically, using the data of game experiments, the proposed framework considers the clustering of game players based on their performance to reflect players’ heterogeneity. Moreover, we also quantify uncertainty within each cluster through statistical modelling and inference. Numerical studies of networked game configurations are conducted to demonstrate the merits of the proposed framework.

The nonmeek inherit the earth: Children generalize dominance, but not submissiveness.

Children and even infants have clear intuitions about power early in development; they can infer who is dominant and subordinate from observing a single interaction. However, it is unclear what children infer about each individual's status from these interactions-do they think dominants and subordinates will maintain their status when interacting with novel partners? In three experiments, we investigate this question. Children (4- to 10-year-olds, N = 365) heard stories about a dominant and subordinate agent and predicted the dominant or subordinate agent's behavior with a novel agent. In all studies, we found that 7- to 10-year-olds generalized dominance, thinking the dominant would again be dominant or "in charge," both for social power (e.g., granting permission) in Study 1 and physical dominance (e.g., a fistfight) in Studies 2 and 3. Furthermore, although they believed dominant agents would win dominance contests (fistfights), they did not believe they would win contests unrelated to dominance (math contests). Younger children did not generalize social power (Study 1) but did generalize physical dominance (Studies 2 and 3). However, even for physical dominance, their generalizations were less selective (i.e., they believed the dominant would win fistfights and math contests). Notably, neither age group generalized an agent's submissiveness in any of the studies-they did not believe a subordinate agent would again be subordinate when paired with a novel partner. We discuss how these results extend past work on children's developing intuitions about dominance and prompt deeper questions about the inferences children draw from dominance interactions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Selective Action Prediction in Infancy Depending on Linguistic Cues: An EEG and Eyetracker Study.

Humans' capacity to predict actions and to socially categorize individuals is at the basis of social cognition. Such capacities emerge in early infancy. By 6 months of age, infants predict others' reaching actions considering others' epistemic state. At a similar age, infants are biased to attend to and interact with more familiar individuals, considering adult-like social categories such as the language people speak. We report that these two core processes are interrelated early on in infancy. In a belief-based action prediction task, 6-month-old infants (males and females) presented with a native speaker generated online predictions about the agent's actions, as revealed by the activation of participants' sensorimotor areas before the agent's movement. However, infants who were presented with a foreign speaker did not recruit their motor system before the agent's action. The eyetracker analysis provided further evidence that linguistic group familiarity influences how infants predict others' actions, as only infants presented with a native speaker modified their attention to the stimuli as a function of the agent's forthcoming behavior. The current findings suggest that infants' emerging capacity to predict others' actions is modulated by social cues, such as others' linguistic group. A facilitation to predict and encode the actions of native speakers relative to foreign speakers may explain, in part, why infants preferentially attend to, imitate, and learn from the actions of native speakers.

How learning-goal-oriented newcomers develop leader-member exchange (LMX): From agentic and communal behavior perspectives

Adopting the dual perspective model of agency and communion and role-making theory of leader–member exchange (LMX) development, this study explored whether and how newcomers with a high learning goal orientation (LGO) develop high-quality social exchange relationships with their leaders. Through a three-wave field survey of 199 organizational newcomers who were graduates entering the workplace, we found a direct and significant relationship between the newcomers’ LGO and LMX. We also found that their agentic and communal behaviors—specifically, self-improvement voice and knowledge sharing, respectively—served as dual mediating mechanisms of this relationship. This study answers the call for more research on how employees’ LGO motivates them to engage in behaviors conducive to social exchange development with their leaders. It also contributes to the emerging research on followership and understanding LMX development beyond the leader–member dyad. The theoretical and practical implications of the findings are discussed.

Collaborative governance in integrated preschool education: A quadrilateral evolutionary game model analysis

The collaborative governance of inclusive preschool education (IPE) is a process in which multiple subjects work together to promote the advancement and development of IPE by taking a series of governance measures. Based on precise identification of multiple stakeholders in collaborative governance of integrated preschool education, it applied the quadrilateral evolutionary game model and Lyapunov's first law to analyze the stability of behavioral strategy choice by multiple subjects in promoting the practice of collaborative governance of IPE and the possible equilibrium points in the system. The influence of key elements on the system evolution is analyzed using MATLAB 2019 simulation. The study reveals that the evolution and trend of one game agent's behavior are closely related to the strategy choice of others. The willingness of the main body of kindergarten, social organization, and parents to participate in the collaborative governance of IPE are related to the government's active promotion to perform its leading functions at the government level. The willingness for collaborative governance of integrated education can help improve mutual trust and the main body's benefits, thus promoting the collaborative behavior of multiple subjects. Complementary resources, strict supervision, reasonable rewards and punishments can promote the collaborative behavior of multiple subjects.

Effects of topological structure and destination selection strategies on agent dynamics in complex networks

We analyzed agent behavior in complex networks: Barabási–Albert, Erdos–Rényi, and Watts–Strogatz models under the following rules: agents (a) randomly select a destination among adjacent nodes; (b) exclude the most congested adjacent node as a potential destination and randomly select a destination among the remaining nodes; or (c) select the sparsest adjacent node as a destination. We focused on small complex networks with node degrees ranging from zero to a maximum of approximately 20 to study agent behavior in traffic and transportation networks. We measured the hunting rate, that is, the rate of change of agent amounts in each node per unit of time, and the imbalance of agent distribution among nodes. Our simulation study reveals that the topological structure of a network precisely determines agent distribution when agents perform full random walks; however, their destination selections alter the agent distribution. Notably, rule (c) makes hunting and imbalance rates significantly high compared with random walk cases (a) and (b), irrespective of network types, when the network has a high degree and high activity rate. Compared with the full random walk in (a) and (b) increases the hunting rate while decreasing the imbalance rate when activity is low; however, both increase when activity is high. These characteristics exhibit slight periodic undulations over time. Furthermore, our analysis shows that in the BA, ER, and WS network models, the hunting rate decreases and the imbalance rate increases when the system disconnects randomly selected nodes in simulations where agents follow rules (a)–(c) and the network has the ability to disconnect nodes within a certain time of all time steps. Our findings can be applied to various applications related to agent dynamics in complex networks.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Investigation of the Thermally Thick Alternative Reducing Agent Behavior in the Raceway Zone

Investigation of the Thermally Thick Alternative Reducing Agent Behavior in the Raceway Zone

Victims of Corruption Suffer Personal and Collective Loss: A Qualitative Study of Public Perceptions

This study aimed to identify citizens’ perceptions of the effects of administrative corruption on their lives. Data were gathered using semi-structured interviews with 15 citizens living in Tehran, Iran, recruited through the convenience sampling method. Two main themes emerged from the data: Negative individual consequences (including “negative emotional consequences”, “negative motivational consequences”, “negative moral and behavioural consequences” and “negative economic consequences”) and Negative collective consequences (including “negative consequences related to family formation and stability” and “negative social consequences”). The study reveals that participants have experienced or perceived adverse effects in the personal, family and social spheres as a result of administrative corruption. The impacts of corruption on people's lives can be taken into consideration when explaining the importance and necessity of designing and implementing preventive measures and policies aimed at reducing corrupt behaviours in public agents.

Demand for Money in the United States: Stability and Forward-Looking Tests

This study shows that demand for money is a function not only of interest rate, real exchange rate, and personal consumption but also of fiscal variables like deficit, debt, and foreign-financed debt. It is stable over the short and long run. This study also covers the investigation of policy invariance of money demand, an important issue ignored so far in the existing literature on the demand for money in the United States. It was found that the behavior of agents in the money market changes as the real exchange rate, consumption, and interest rate change. Namely, agents in the money market are forward-looking, and their expectations are formed rationally. This also means that even though the parameters of money demand are stable, according to the stability test results, they can be unstable, as agents adapt their behavior based on any change in the exchange rate, consumption, and/or interest rate. In other words, the contemporaneous real exchange and interest rate variables are not superexogenous in demand for M1, and the contemporaneous consumption is not superexogenous in demand for M2.

A multimodal dialogue system for customer service based on user personality adaptation and dialogue strategies

ABSTRACT This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression.

Entry regulations with implementation lag: Evidence from convenience store markets in Korea

Seocho County in Korea attempted to advance the interests of incumbent businesses by weakening competition in the convenience store market. To this end, it created a regulation impeding the opening of new convenience stores. Although this regulation intensified proximity restrictions between stores, our findings reveal that the announcement of legislation condemned incumbent businesses to unfavorable survival probabilities. We provided supporting evidence that so-called implementation lags—time lags between the announcement and implementation of policies—and adaptive behaviors of potential store owners may have caused unintended consequences. Our findings imply that policymakers should consider implementation lag and the adaptive behaviors of economic agents.

ESARS: A Situation-Aware Multi-Agent System for Real-Time Emergency Response Management

This paper describes ESARS, a real-time situation-aware social media- enabled emergency situation alert and reporting system, as a decision support system built on multi-agent software design architecture for emergency situation management. The impact of an incident or disruption due to the incident could be minimized by implementing real-time intervention strategies that involve event monitoring, detection and situation identification via classification and prediction, notification, visualization and reporting that culminate in providing emergency support within time. The nature of agent behavior, which is autonomous, proactive and cooperative, makes them a suitable method for the design and deployment of a dynamic system of this nature. The system relies on historical and streamed real-time geolocation-enabled Twitter data stream for the target emergency events to provide decision-makers with dynamic, comprehensive, and timely information specific to the emergency situation.

Assessing the impact of isolation policies on epidemic dynamics through swarm entropy.

Isolation policies are an effective measure in epidemiological models for the prediction and prevention of infectious diseases. In this paper, we use a multi-agent modeling approach to construct an infectious disease model that considers the influence of isolation policies. The model analyzes the impact of isolation policies on various stages of epidemic from two perspectives: the external environment and agents behavior. It utilizes multiple variables to simulate the extent to which isolation policies influence the spread of the pandemic. Empirical evidence indicates that the progression of the epidemic is primarily driven by factors such as public willingness and regulatory intensity. The improved model, in comparison to traditional infectious disease models, offers greater flexibility and accuracy, addressing the need for frequent modifications in fundamental models within complex environments. Meanwhile, we introduce "swarm entropy" to evaluate infection intensity under various policies. By linking isolation policies with swarm entropy, considering population structure, we quantify the effectiveness of these isolation measures. It provides a novel approach for complex population simulations. These findings have facilitated the enhancement of control strategies and provided decision-makers with guidance in combating the transmission of infectious diseases.

Life-cycle decisions and general equilibrium in the heterogeneous-agent OLG economy

ABSTRACT This study develops a life-cycle model to predict an overlapping generations economy with heterogeneous agents. In our proposed model, ex-ante heterogeneity includes the work probability, survival rate, subjective discount rate, and intertemporal elasticity of substitution. We observe differences in financial decisions and asset accumulation among heterogeneous agents and calculate equilibrium values of aggregate economic variables, such as consumption and capital. For the numerical analyses, we classify the population into four groups based on labour productivity and gender, and investigate the life-cycle behaviours of heterogeneous agents. Retirees in the male group with low labour productivity and low remaining life expectancy have the highest propensity to consume and the highest consumption elasticity for retirement shock compared to other retirees. Workers with relatively high labour productivity have lower risk adjustment factors, indicating that they are less concerned about retirement risk than less productive workers.

Synthesis and performance of a new long-acting slump retention agent at different temperatures

Under high-temperature conditions, the fluidity retention properties of polycarboxylate superplasticizers remain a challenge. This study improved the molecular structure of traditional slow-release polycarboxylate superplasticizers (also known as slump-retention agents) by introducing hydroxypropyl acrylate, successfully synthesizing a novel slump-retention agent that maintains concrete slump for an extended period at high temperatures. Comparing its performance with two other conventionally synthesized slump-retention agents, the superior performance of the new agent was verified. We then delved into the behavior of this novel slump-retention agent under different temperature conditions.

Explaining the Behaviour of Reinforcement Learning Agents in a Multi-Agent Cooperative Environment Using Policy Graphs

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last few years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we apply an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first use case of policy graphs in the context of explaining agent behaviour in cooperative multi-agent scenarios and present experimental results that sets this kind of scenario apart from previous implementations in single-agent scenarios: when requiring cooperative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.

Disposition effect and its outcome on endogenous price fluctuations

AbstractWe have developed a financial market model that incorporates the Disposition Effect, which refers to traders’ tendency to avoid realizing losses. Specifically, our model replicates several stylized facts commonly observed in financial markets, such as fat tails and volatility clustering. These market characteristics can be attributed to the Disposition Effect, especially when the trading behavior of agents aligns with the findings of Ben-David and Hirshleifer (Rev Financ Stud 25(8):2485–2532, 2012). To demonstrate this, we examine two versions of the model: one where a class of agents exhibits a high degree of Disposition Effect and another where traders are not influenced by it. By comparing the simulated time series generated by both versions, we find that the one with agents affected by the Disposition Effect better replicates the features observed in real financial markets. This holds true for both the deterministic and stochastic versions of the model.

Determinants of the Behavior of Households in Ouagadougou Regarding the Choice of Cooking Energy

The Burkinabe energy context is characterized by a predominance of the use of biomass energies, the country's dependence on fossil fuels, low and inequitable access to modern energies and very low valorization of endogenous renewable energies. Households use these different types of energy for cooking, heating food and heating water. The choice of these energies depends on several criteria. The analysis of the characteristics which can influence this choice is based on the discrete choice model resulting from the hypothesis of utility maximization behavior of the economic agent. This analysis reveals that certain household characteristics including household size, high income and the level of education of the head of household have an effect on energy choice although it is weak. The environmental criterion has no effect on the choice of households and the observation is that the choice of households is mainly based on their desire for comfort. The environmental reason is rarely mentioned. Given the weakness of the vegetation cover in the country, it would be appropriate to intensify the supervision of the wood sector, to strengthen the actions already undertaken (reduction of wood consumption, promotion of energy alternatives, safeguarding and restoration of forests) and the capacities of the actors and finally placed great emphasis on the need to provide quality education to raise popular awareness of environmental issues.