Behavior Of Agents Research Articles

Hope and life satisfaction in Palestinian children victim of military violence: The predictive role of agency, potentially traumatic experiences and symptoms of trauma

BackgroundChildren exposed political violence deploy resources to maintain functioning, hope and life satisfaction. ObjectiveWe sought to explore whether or not children promote hope and life satisfaction trough agency, psychological difficulties, potentially traumatic experiences and symptoms in Palestine. Participants and setting965 children (494 males and 471 females) in multiple geographical contexts, and areas were involved. MethodsWe administered the War Child Agency Assessment Scale, Child Hope Scale, Multilevel Students'Life Satisfaction Scale-Bref, the Strength and difficulties scale, the Child Revised Impact of events Scale, and Trauma Checklist, and performed regression analysis; hope and life satisfaction were dependent and agency, strength and difficulties, trauma symptoms and traumatic events independent variables. ResultsSpecific forms of agency predicted life satisfaction (β = 0.219; ** p < .01, social agency; β = 0.11; ** p < .01, with agency in education) and hope (β = 0.07; ** p < .05, agency on free movement), while mental difficulties (conduct problems, β = −0.09; ** p < .01; hyperactivity, β = −0.07; ** p < .05; β = −0.15; ** p < .01 with life satisfaction) (conduct problems, β = −0.06; ** p < .05, and difficulties in pro-social behaviour, β = −0.21; ** p < .01 with hope), traumatic events (β = −0.16; ** p < .01, with life satisfaction; β = −0.15; ** p < .01, with hope) and trauma symptoms (β = −0.09; ** p < .05, with hope) were negatively associated with the dependents variables. ConclusionsWe found a positive role of social, educational, and freedom of movement agentic behaviours in fostering hope and life satisfaction.

Distributed Coordination of Charging Stations With Shared Energy Storage in a Distribution Network

Electric vehicle (EV) charging stations have experienced rapid growth, whose impacts on the power grid have become non-negligible. Though charging stations can install energy storage to reduce their impacts on the grid, the conventional “one charging station, one energy storage” method may be uneconomical due to the high upfront cost of energy storage. Shared energy storage can be a potential solution. However, effective management of charging stations with shared energy storage in a distribution network is challenging due to the complex coupling, competing interests, and information asymmetry between different agents. To address the aforementioned challenges, this paper first proposes an equilibrium model to characterize the interaction among charging stations, shared energy storage, and the distribution network. We prove that the equilibrium coincides with the centralized optimization result with trading prices equaling the value of dual variables at optimum. Then, to achieve the efficient equilibrium, a distributed coordination mechanism with a prediction and a correction step is developed to guide the behaviors of different agents with proof of convergence. Numerical experiments and comprehensive performance comparisons are conducted to validate the theoretical results and show the advantages of the proposed mechanism.

Future Bias and Regret

Although the rationality of future bias figures crucially in various metaphysical and ethical arguments (Prior 1959; Parfit 1984; Fischer 2020), many philosophers have challenged future bias as being either arbitrarily motivated or irrational (Dougherty 2011; Suhler and Callender 2012; Greene and Sullivan 2015). In particular, Greene and Sullivan (2015) have claimed that future bias is irrational because it implicates two kinds of irrational planning behaviors in agents who seek to avoid regret. In this paper, I join others (Dorsey 2016; Tarsney 2017) in arguing against their claims, but for different reasons that highlight the relationship between the alleged irrational planning behaviors and certain features of regret that it shares with future bias. First, regret is dynamic, involving preferences that change over time and in inconsistent ways. Second, regret comes in degrees, meaning that we can rank our potential regrets. Because regret has these features, I explain why the future-biased agents in Greene and Sullivan’s cases do not need to act in irrational ways to avoid regret.

A hierarchical multi-agent allocation-action learning framework for multi-subtask games

Great progress has been made in the domain of multi-agent reinforcement learning in recent years. Most work concentrates on solving a single task by learning the cooperative behaviors of agents. However, many real-world problems are normally composed of a set of subtasks in which the execution order follows a certain procedure. Cooperative behaviors should be learned on the premise that agents are first allocated to those subtasks. In this paper, we propose a hierarchical framework for learning the dynamic allocation of agents among subtasks, as well as cooperative behaviors. We design networks corresponding to agents and subnetworks, respectively, which constitute the whole hierarchical network. For the upper layer, a novel allocation learning mechanism is devised to map an agent network to a subtask network. Each agent network could only be assigned to only one subtask network at each time step. For the lower layer, an action learning module is designed to compute appropriate actions for each agent with the allocation result. The agent networks together with the subtask networks are updated by a common reward obtained from the environment. To evaluate the effectiveness of our framework, we conduct experiments in two challenging environments, i.e., Google Research Football and SAVETHECITY. Empirical results show that our framework achieves much better performance than other recent methods.

Determinants of Indonesian Gen Z’s purchase behavior on online travel platforms: Extending UTAUT model

Understanding Gen Z’s purchase behavior in online travel agents is essential to effectively engage and meet the unique preferences of this generation, fostering long-term loyalty and satisfaction. Utilizing the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation, this study aims to analyze the impact of performance expectancy, effort expectancy, social influence, facilitating condition, and trust on purchase decision of flight tickets through online travel agent platforms. The data were collected through an online survey of 253 Gen Z users of online travel agent applications in Indonesia, such as Traveloka, Tiket.com, Pegipegi.com, Agoda, and Booking.com. The study employed PLS-SEM to test the hypotheses. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions influence trust (t-value 1.645, p-value &amp;amp;lt; 0.05). Further, performance expectancy, effort expectancy, and facilitating conditions influence purchase decisions (t-value 1.645, p-value &amp;amp;lt; 0.05). However, social influence does not significantly affect purchase decisions (t-value 1.041, p-value &amp;amp;gt; 0.05). The analysis also shows that trust fully mediates the relationship between social influence and purchase decisions, while no mediating effect is identified in the relationship between effort expectancy and purchase decisions. By investigating the key factors contributing to Gen Z’s buying behavior in online travel agent platforms, this paper provides valuable insights for online travel businesses to effectively engage and cater to Gen Z’s unique needs and preferences.

Crowd model calibration at strategic, tactical, and operational levels: Full-spectrum sensitivity analyses show bottleneck parameters are most critical, followed by exit-choice-changing parameters

ABSTRACT Crowd motion simulation requires specification of a range of parameters, each reflecting certain aspects of agent behavior. But what parameters matter the most? Are they all equally important? The question is important given that available data and resources for parameter calibration are limited, and priorities often need to be made. Here, for the first time, a full-spectrum sensitivity analysis of crowd model parameters is reported. It is shown that estimates of simulated evacuation time are, by far, most dependent on the value of locomotion/operational parameters, especially those that determine discharge rate at bottlenecks. The next most critical set of parameters are those that influence change of direction choices. If a crowd simulation model fails to reproduce bottleneck flows accurately, efforts to refine other modeling layers will be in vain. Similarly, if the model fails to represent exit choice adaptation/changing accurately, efforts to refine the exit choice model will be fruitless.

Unique portal venous phase imaging discordance between CEUS and MRI: a valuable predictor of intrahepatic cholangiocarcinoma?

We have long noted unique portal venous phase (PVP) imaging discordance of focal liver masses between CEUS, showing rapid marked washout, and MRI, showing progressive or sustained enhancement. We postulate association of this unique discordance with intrahepatic cholangiocarcinoma (ICC) and causal relationship to different contrast agent behavior. We investigate this unique discordance, propose its clinical significance for ICC diagnosis, and confirm further histologic associations. Cases were collected within our CEUS department and from pathology records over a ten-year interval. This retrospective review includes 99 patients, 73 with confirmed ICC and 26 other diagnoses,showing unique PVP discordance. The CEUS and MRI enhancement characteristics were compared for all patients. Unique discordance is identified in 67/73 (92%) ICC and difference between the PVP appearance on MRI and CEUS is statistically significant (p< 0.0001). Arterial phase enhancement did not show statistically significant difference between CEUS and MRI, p> 0.05. Other diagnoses showing unique discordance include especially lymphoma (n=7), sclerosed hemangioma (n=6), HCC (n=4), metastases (n=2), and other rare entities. ICC shows this discrepant intermodality enhancement pattern in a statistically significant number of cases and should be considered along with other LR-M features in at-risk patients. Discordance is also rarely seen in a number of other liver lesions.

Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments

Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effect is even more significant, as unexpected behavior cause accidents with potentially grave and long-lasting consequences for the involved individuals. In this work, we propose using program synthesis to imitate reinforcement learning policies after seeing a trajectory of the action sequence. Programs have the advantage that they are inherently interpretable and verifiable for correctness. We adapt the state-of-the-art program synthesis system DreamCoder for learning concepts in grid-based environments, specifically, a navigation task and two miniature versions of Atari games, Space Invaders and Asterix. By inspecting the generated libraries, we can make inferences about the concepts the black-box agent has learned and better understand the agent's behavior. We achieve the same by visualizing the agent's decision-making process for the imitated sequences. We evaluate our approach with different types of program synthesizers based on a search-only method, a neural-guided search, and a language model fine-tuned on code.

Story Shaping: Teaching Agents Human-Like Behavior with Stories

Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about how that effect is achieved. For example, we might wish for an agent to adhere to a tacit understanding of commonsense, align itself to a preference for how to behave for purposes of safety, or taking on a particular role in an interactive game. Storytelling is a mode for communicating tacit procedural knowledge. We introduce a technique, Story Shaping, in which a reinforcement learning agent infers tacit knowledge from an exemplar story of how to accomplish a task and intrinsically rewards itself for performing actions that make its current environment adhere to that of the inferred story world. Specifically, Story Shaping infers a knowledge graph representation of the world state from observations, and also infers a knowledge graph from the exemplar story. An intrinsic reward is generated based on the similarity between the agent's inferred world state graph and the inferred story world graph. We conducted experiments in text-based games requiring commonsense reasoning and shaping the behaviors of agents as virtual game characters.

Interaction state Q-learning promotes cooperation in the spatial prisoner's dilemma game

Many recent studies have used reinforcement learning methods to investigate the behavior of agents in evolutionary games. Q-learning, in particular, has become a mainstream method during this development. Here we introduce Q-learning agents into the evolutionary prisoner's dilemma game on a square lattice. Specifically, we associate the state space of Q-learning agents with the strategies of their neighbors, and we introduce a neighboring reward information sharing mechanism. We thus provide Q-learning agents with the payoff information of their neighbors, in addition to their strategies, which has not been done in previous studies. Through simulations, we show that considering neighborhood payoff information can significantly promote cooperation in the population. Moreover, we show that for an appropriate strength of neighborhood payoff information sharing, a chessboard pattern emerges on the lattice. We analyze in detail the reasons for the emergence of the chessboard pattern and the increase in cooperation frequency, and we also provide a theoretical analysis based on the pair approximation method. We hope that our research will inspire effective approaches for resolving social dilemmas by means of sharing more information among reinforcement learning agents during evolutionary games.

Risk Management: Anticipating and Reacting in StarCraft

Managing risk with imperfect information is something humans do every day, but we have little insight into the abilities of AI agents to do so. We define two risk management strategies and perform an ability-based evaluation using StarCraft agents. Our evaluation shows that nearly all agents mitigate risks after observing them (react), and many prepare for such risks before their appearance (anticipate). For this evaluation, we apply traditional causal effect inference and causal random forest methods to explain agent behavior. The results highlight different risk management strategies among agents, others strategies that are common to agents, and overall encourage evaluating agent risk management abilities in other AI domains.

Thermal decomposition behavior and kinetics of rock-breaking incendiary agent based on peanut shell

Thermal decomposition behavior and kinetics of rock-breaking incendiary agent based on peanut shell

Agent Behavior Collaborative Strategy in Cocoa Agroindustry in Central Java, Indonesia

Objective: This study aims to influence the behavior of agro-industry actors in meeting the demand for cocoa beans in Batang Regency, Central Java, Indonesia. Methods: From a similar synthesis of literature, 38 indicators of collaborative behavior were produced which were used to create a questionnaire, and the data was collected by directly observing cocoa agro-industry actors—farmers, village collectors, sub-district collectors, large collectors, and cocoa factories. Partial Least Square (PLS) is used in data processing to identify collaborative behavior and indicators that influence behavior, and the Fuzzy Analytical Hierarchy Process (FAHP) is used for weighting. Results: The PLS results explain that the behavior of "trust" greatly influences communication behavior. That is, communication goes well when the agro-industry actor's trust system is high. "Information-sharing" behavior has a large impact on collaboration and "collaborative" behavior has a large impact on "continuous improvement". FAHP results obtained weighting values, and the order of priority shows that behaviors with the highest weighting values are trust between members of the supply chain, continuous communication, information sharing, value relationships, collaboration, and continuous improvement. In this results, each of these things needs to be considered for the smooth flow of distribution at the microscopic level in supporting the performance improvement of the cocoa agro-industry, and for other agro-industry.

Marxian Analysis of Capitalism and Crises

ABSTRACT The subject of economics, which was once famous for several views and schools of thought, has been limited to just one school neoclassical: where markets usually find equilibrium, meaning government intervention is not needed, and any such intervention could hinder the economic growth process and smooth functioning of the economy. The neoclassical theory focuses on the behaviour of individual agents, which are assumed to be economic decision-makers. There is an attempt to emphasise the importance of the Marxist approach to analysing capitalist crises logically and historically. The study of economics must stay close and keep unity between different social sciences, especially sociology, political science, and history, which classical economists had established but marginalist economists had tried to undermine. The study concludes that economics is embedded in society and politics, and the mainstream school of thought ignores this. They cannot effectively address the main economic issues of our times, which include combating rising inequality, economic and environmental crises, and biodiversity loss.

PENGARUH KARAKTERISTIK PERUSAHAAN DAN DEWAN KOMISARIS TERHADAP PENGUNGKAPAN ANTI KORUPSI PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA

The anti-corruption program is the company's initial defense strategy against corruption. Therefore, the disclosure and transparency of these programs is a commitment in illuminating corruption and improving the ethical behavior of management, employees, partners, agents and other related parties of the company. This study aims to analyze the effect of company characteristics and the board of commissioners on corporate anti-corruption disclosures. The research objects are manufacturing sector companies listed on the IDX in 2020 and 2021. The sampling method is purposive sampling with a sample of 100 companies. Data collection uses research instruments, data analysis is quantitative/statistical in nature with the aim of testing the established hypotheses. Methods of data analysis using SPSS version 25.0. This study found that company size, company age, net profit margin, and board of commissioners have a positive effect on anti-corruption disclosure.&#x0D; Keywords: Anti-corruption Disclosure, Company Characteristics, Board of Commissioners, Manufacturing Companies

A Repeated Unknown Game: Decentralized Task Offloading in Vehicular Fog Computing

Offloading computation to nearby edge/fog computing nodes, including the ones carried by moving vehicles, e.g., vehicular fog nodes (VFN), has proved to be a promising approach for enabling low-latency and compute-intensive mobility applications, such as cooperative and autonomous driving. This work considers vehicular fog computing scenarios where the clients of computation offloading services try to minimize their own costs while deciding which VFNs to offload their tasks. We focus on decentralized multi-agent decision-making in a repeated unknown game where each agent, e.g., service client, can observe only its own action and realized cost. In other words, each agent is unaware of the game composition or even the existence of opponents. We apply a completely uncoupled learning rule to generalize the decentralized decision-making algorithm presented in [7] for the multi-agent case. The multi-agent solution proposed in this work can capture the unknown offloading cost variations susceptive to resource congestion under an adversarial framework where each agent may take implicit cost estimation and suitable resource choice adapting to the dynamics associated with volatile supply and demand. According to the evaluation via simulation, this work reveals that such individual perturbations for robustness to uncertainty and adaptation to dynamicity ensure a certain level of optimality in terms of social welfare, e.g., converging the actual sequence of play with unknown and asymmetric attributes and lowering the correspondent cost in social welfare due to the self-interested behaviors of agents.

A Multi-Agent System in Education Facility Design

This paper deals with a multi-agent system which supports the designer in solving complex design tasks. The behaviour of design agents is modelled by sets of grammar rules. Each agent uses a graph grammar or a shape grammar and a database of facts concerning the subtask it is responsible for. The course of the design process is determined by the interaction between specialised agents. Space layouts of designs are represented by attributed graphs encoding both topological structures and semantic properties of solutions. The agents work in parallel on the common graph, independently generating layouts of different design components while specified node labels evoke agents using shape grammars. The agents’ cooperation allows them to combine a form-oriented approach with a functional-structural one in the design process, where the agents generate the general 3D form of the object based on design requirements together with the space layout based on the functional aspects of the solution. Based on the given design criteria, the agents search for admissible solutions within the design space that constitutes their operating environment. The proposed approach is illustrated by the example of designing kindergarten facilities.

A Framework for Defining and Analyzing Agrarian Governance

The problem of adequate understanding and evaluation of the system of agrarian governance is among the most topical academic and practical tasks. The goal of the article is to adapt the interdisciplinary New Institutional Economics and to propose an adequate definition and framework for analyzing the system of agrarian governance in Bulgaria. Based on a critical analysis of research and practical experience, it is underlined that agrarian governance is to be studied as a complex system, including four principle components: agrarian and related agents involved in management decision-making; rules, forms and mechanisms that govern the behavior, activities and relationships of agrarian agents; processes and activities related to making governing decisions; a specific social order resulting from the governing process and functioning of the system. The analysis of agrarian governance should include the individual elements of the system, different levels of governance and the main functional areas of agrarian governance, for each of which adequate quantitative or qualitative methods from the institutional approach are suggested. The assessment must take into account the personal characteristics of agents, the institutional environment, transaction costs, the comparative effectiveness of alternative structures, and the “time factor”.

Mind the Doxastic Space: Examining the Social Epistemology of the Ethiopian Wax and Gold Tradition

The wax and gold tradition is mainly known as an Ethiopian literary system that plays with layers of meanings. It has also established itself as a system of knowledge and/or belief production and validation. However, its social ramifications have presented scholars with conundrums that divide their views. For some, it is an Ethiopian traditional society’s crowning achievement of erudition—a poetic form that infiltrated communication, psychology, and social interaction. For others, it is a breeding ground for social vices, i.e., mutual suspicion, deception, duplicity, etc., because its autochthonous nature means it is inept in terms of modernizing and unifying the society. In this essay, I aim to argue that there is one critical historical element that holds the key to the conflicting social ramifications of the wax and gold system and, yet, is neglected by both sides of the debate: the original doxastic space of qine (poetry) and sem ena werq (wax and gold system)—a hermeneutic tool that deciphers the meaning of poems. This literary system was born in the space of worship and liturgy. I will contend, therefore, that a shift of doxastic space from sacred to saeculum (the world) is the reason not only for the behavior of doxastic agents but also for the social outcome of the knowledge they create.

Leveraging a multivariate approach towards enhanced development of direct compression extended release tablets

Extended release formulations play a crucial role in the pharmaceutical industry by maintaining steady plasma levels, reducing side effects, and improving therapeutic efficiency and compliance. One commonly used method to develop extended release formulations is direct compression, which offers several advantages, such as simplicity, time savings, and cost-effectiveness. However, successful direct compression-based extended release formulations require careful assessment and an understanding of the excipients’ attributes. The scope of this work is the characterization of the compaction behavior of some matrix-forming agents and diluents for the development of extended release tablets. Fifteen excipients commonly used in extended release formulations were evaluated for physical, compaction and tablet properties. Powder properties (e.g., particle size, flow properties, bulk density) were evaluated and linked to the tablet’s mechanical properties in a fully integrated approach, and data were analyzed by constructing a principal component analysis (PCA). Significant variability was observed among the various excipients. The present work successfully demonstrates the applicability of PCA as an effective tool for comparative analysis, pattern and clustering recognition and correlations between excipients and their properties, facilitating the development and manufacturing of direct compressible extended release formulations.