Model Of Adaptive Behavior Research Articles

Impact of the Elementary School Heads Leadership Behavior on the Teacher’s Morale in School Districts of Pilar

The study focused on elementary school heads' leadership behaviors and their impact on teacher morale in Pilar's school districts, aiming to provide valuable insights into educational leadership dynamics. It outlines the objectives of analyzing leadership behavior, assessing teacher morale, determining impacts, identifying challenges, describing measures, and proposing a leadership model to address these issues comprehensively. The mixed-methods study used surveys and document analysis to examine how leadership behavior influenced teacher morale. Quantitative methods measured the impact on commitment and assessed various attitudes and challenges. Qualitative methods analyzed leadership behaviors reflected in OPCRF and described measures to address challenges. Findings indicate proactive leadership practices, contributing to positive teacher morale, yet challenges such as workload and communication gaps persist, indicating areas for improvement. Measures to address these challenges include embracing technology, fostering collaboration, and enhancing communication channels to create a supportive and conducive work environment. The proposed Adaptive Transformational School Leadership Behavior and Change Model integrates adaptive and transformational leadership theories, offering a holistic framework to address challenges and drive positive change within educational contexts. In conclusion, the study underscores the crucial role of school leadership in shaping teacher morale and organizational dynamics, highlighting the importance of supportive environments, effective communication, and targeted interventions to enhance educational outcomes and foster a conducive learning environment within Pilar's elementary schools.

Study of algorithms for tracing connections between pins

Tracing is mathematically the most complicated problem of choosing the optimal solution from a huge number of variants. The development of methods and algorithms for solving the tracing problem has been carried out for many years, but it is still urgent. It is connected, first of all, with the fact that this problem is NP-complete, and it is difficult to develop a universal algorithm allowing one to find the exact optimal solution for an acceptable time. The emergence of new and more advanced means of computing technology, giving powerful computing resources, as well as increased requirements to the designed devices, all this is an incentive to develop new algorithms for solving the problem of tracing. There are several approaches to solving NP -complete problems. The first class of algorithms includes, explicitly or implicitly providing for the possibility of exponential running time of the algorithm, such methods as the method of complete enumeration, linear and nonlinear programming, etc. The second class includes the so-called heuristic algorithms that allow to obtain good solutions in an acceptable time. The third class includes random-directed search algorithms based on modeling principles (Lebedev, 2012). We propose a swarm algorithm for reallocating connections between leads based on the integration of models of adaptive behavior of an ant colony and collective alternative adaptation. The essence of integration consists of the fact that during the execution of the search procedure, the alternation of separate procedures of the ant algorithm and collective alternative adaptation is performed. Experimental studies were carried out that confirm the effectiveness of the proposed paradigm.

Explaining and predicting animal migration under global change

AbstractMany migratory species are declining due to global environmental change. Yet, their complex annual cycles make unravelling the impacts of potential drivers such as climate and land‐use change on migrations a major challenge. Identifying where, when and how threatening processes impact species' migratory journeys and population dynamics is crucial for identifying effective conservation actions. Here, we describe how a new migration modelling framework – Spatially explicit Adaptive Migration Models (SAMMs) – can simulate the optimal behavioural decisions required to migrate across open land‐ or seascapes varying in character over space and time, without requiring predefined behavioural rules. Models of adaptive behaviour have been used widely in theoretical ecology but have great untapped potential in real‐world contexts. Applying adaptive behaviour models across open environments will allow users to explore how migratory species may adapt their routes and usage of intermediate sites in response to environmental change. We outline how SAMMs can be used to model migratory journeys through aerial, terrestrial and aquatic environments, demonstrating their potential using a case study on the common cuckoo (Cuculus canorus) and comparing modelled to observed behaviours. SAMMs offer a tool to identify the key threats faced by migratory species, how they could adapt to future migratory journeys in response to changing environmental conditions and the consequences of not being able to adapt to change.

Prediction error in models of adaptive behavior

Pavlovian conditioning is evident in every species in which it has been assessed, and there is a consensus about its interpretation across behavioral,1,2 brain,3,4,5,6 and computational analyses7,8,9,10,11: conditioned behavior reflects the formation of a directional associative link from the memory of one stimulus (e.g., a visual stimulus) to another (e.g., food), with learning stopping when there is no error between the prediction generated by the visual stimulus and what happens next (e.g., food). This consensus fails to anticipate the results that we report here. In our experiments with rats, we find that arranging predictive (visual stimulus→food) and nonpredictive (food→visual stimulus) relationships produces marked and sustained changes in conditioned behaviors when the visual stimulus is presented alone. Moreover, the type of relationship affects (1) the distribution of conditioned behaviors related to the properties of both food (called goal-tracking) and the visual stimulus (called sign-tracking) and (2) when in the visual stimulus, these two behaviors are evident. These results represent an impetus for a fundamental shift in how Pavlovian conditioning is interpreted: animals learn about the relationship between two stimuli irrespective of the order in which they are presented, but they exhibit this knowledge in different ways. This interpretation and our new results are captured by a recent model of Pavlovian conditioning,12,13 HeiDI, and both are consistent with the need for animals to represent the fact that the impact of a cause (e.g., the ingestion of nutrients or the bite of a predator) can be felt before or after the cause has been perceived.

Suboptimal foraging theory: How inaccurate predictions and approximations can make better models of adaptive behavior.

Optimal foraging theory (OFT) is based on the ecological concept that organisms select behaviors that convey future fitness, and on the mathematical concept of optimization: finding the alternative that provides the best value of a fitness measure. As implemented in, for example, state-based dynamic modeling, OFT is powerful for one key problem of modern ecology: modeling behavior as a tradeoff among competing fitness elements such as growth, risk avoidance, and reproductive output. However, OFT is not useful for other modern problems such as representing feedbacks within systems of interacting, unique individuals: When we need to model foraging by each of many individuals that interact competitively or synergistically, optimization is impractical or impossible-there are no optimal behaviors. For such problems we can, however, still use the concept of future fitness to model behavior by replacing optimization with less precise (but perhaps more realistic) techniques for ranking alternatives. Instead of simplifying the systems we model until we can find optimal behavior, we can use theory based on inaccurate predictions, coarse approximations, and updating to produce good behavior in more complex and realistic contexts. This so-called state- and prediction-based theory (SPT) can, for example, produce realistic foraging decisions by each of many unique, interacting individuals when growth rates and predation risks vary over space and time. Because SPT lets us address more natural complexity and more realistic problems, it is more easily tested against more kinds of observation and more useful in management ecology. A simple foraging model illustrates how SPT readily accommodates complexities that make optimization intractable. Other models use SPT to represent contingent decisions (whether to feed or hide, in what patch) that are tradeoffs between growth and predation risk, when both growth and risk vary among hundreds of patches, vary unpredictably over time, depend on characteristics of the individuals, are subject to feedbacks from competition, and change over the daily light cycle. Modern ecology demands theory for tradeoff behaviors in complex contexts that produce feedbacks; when optimization is infeasible, we should not be afraid to use approximate fitness-seeking methods instead.

To isolate or not to isolate: the impact of changing behavior on COVID-19 transmission

BackgroundThe COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. In early days of the pandemic, neither vaccines nor therapeutic drugs were available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only.MethodsWe construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics).ResultsThis results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high.ConclusionsTo reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.

Model of adaptive behavior of enterprises in the context of transformational changes associated with the aggravation of the climate crisis

It is stated that in the current conditions of the activity of enterprises, the external environment acts as a catalyst for adaptive organizational changes, which in recent years has been increasingly acquiring cardinal transformations, thereby predetermining the VUCA conditions for the functioning of business entities, which requires the mandatory formation of an adaptation policy by them, as an integral part of the management system. and fundamental basis for further development. It is noted that ensuring the effectiveness of the adaptation policy, as practice shows, is possible only with the right choice of strategy and model of adaptive behavior of enterprises aimed at achieving certain guidelines for their development. It has been substantiated that the specificity of the modern conditions of managing enterprises determines the expediency of an organic synthesis of the theory of adaptation with the theory of strategic management. It is emphasized that the adaptation of the existing potential to the requirements and needs of the future involves the formation of an adaptation strategy by enterprises, which, in the conditions of accelerated transformations, in particular, as a result of the exacerbation of the climate crisis, should be determined according to the scenario approach, which will allow building a portfolio of the external environment, and, consequently, a vision of the expected the prospects for its changes on short-term and long-term time horizons. The definitions of the interpretation of the concept of “adaptation” are considered and the approaches to their formation are analyzed. It has been proved that the strategic management of enterprises, adapted to economic transformations due to climate change, should focus primarily not on solving existing problems, but on using the opportunities and strengths of the enterprise. It is emphasized that, depending on the role and importance of adaptive elements, as well as readiness for an adaptive response, it is customary to distinguish three models of behavior of enterprises, each of which determines the readiness for adaptation: a model of active, conservative and mixed behavior. Each of the models of adaptive behavior of enterprises is analyzed. The need for accelerated climate change and transformations in the economy to apply a mixed model of adaptive behavior of enterprises is substantiated.

Концептуальные подходы и программа полимасштабного исследования миграции иностранных студентов в России

Introduction. Student immigration is considered as one of the most active forms of youth spatial mobility. The main task of the study is to develop conceptual approaches and a program for studying the spatial features of student immigration in Russia. Materials and research methods. The study is based on the analysis of extensive literature materials that reveal the main theoretical and methodological provisions of the analysis of educational migrations. Their strengths and weaknesses have been established, the need to develop new approaches related to identifying the spatial aspects of international student migration has been proved. Results of the study and their discussion. Based on the key theoretical and methodological provisions and the revealed facts, taking into account our own proposals and accumulated experience, a conceptual scheme and a program for studying the features of the spatial differentiation of student migration in Russia have been developed. As core areas of research, the following are proposed: identifying spatio-temporal features of development and differentiation of processes associated with the immigration of foreign students at the global, country and regional-local levels; establishing positional factors influencing decision-making when choosing places to receive higher education abroad; disclosure of intra-country inequality in the distribution of foreign students depending on the characteristics of the sending countries and receiving regions; detection of country and regional models of adaptive behavior of foreign students. In accordance with the logic of the developed concept, a program for studying the spatial features of the immigration of foreign students in Russia is presented. The step-by-step algorithm of the research program includes several stages, reflecting the nature and content of the main tasks to be solved. Conclusions. An analysis of the state of the theoretical and methodological base on the problem of migration of foreign students made it possible to establish the lack of elaboration of aspects related to the spatial features of their transformation and, in particular, the adaptation of foreign students in host countries. The proposed conceptual approaches and research program are aimed at filling the identified gaps.

Complex human-deer interactions challenge conventional management approaches: the need to consider power, trust, and emotion

In the United States, the management of white-tailed deer (<em>Odocoileus virginianus</em>) has typically focused on improving hunting opportunities and mitigating human-deer conflicts. Yet the expansion and diversification of human communities and activities implies that human-deer interactions may also be diversifying. Approaches based on complex adaptive systems theories have been posited as a way to better attend to the diversity of these interactions between humans and wildlife. Using Indiana as a case, this study draws from the Integrated Adaptive Behavior Model (IABM) to understand human-deer interactions as a complex system. We use empirical social science to understand how citizens across Indiana perceive deer populations, what outcomes they desire, and how these perceptions could be integrated into Indiana’s deer management plan. In Indiana, neither wildlife managers nor researchers have assessed public perceptions of deer beyond hunting and farming stakeholders. From May to September 2019, we collected 59 semi-structured interviews and two focus groups (n = 14) with deer stakeholders including woodland owners, farmers, deer hunters, and urban area residents. Through mixed inductive-deductive coding, we found that Indiana citizens hold complex emotions toward deer regardless of their stakeholder identity. Factors influencing these emotions include past experiences, current livelihood and behavioral contexts, beliefs about responsibilities and ethics in deer management, and beliefs about other social groups. Our results suggest that the IABM, despite adding in much-needed complexity and realism to the analysis of human-wildlife interactions, still lacks explanatory power over several important dynamics that emerged from our interviews. Here, we discuss how mixed emotions, situational context, and power dynamics challenge conventional management approaches that focus narrowly on mitigating human-deer conflicts, and that reduce public interests to demographic categorizations. To better inform social-ecological governance, models of complex human behavior should account for power within management institutions and across management scales. Our work contributes a refined understanding of how multidimensional emotions and experiences influence public (dis)interest in natural resource management, and what this implies for managers who aim to balance competing social interests with ecological conditions.

On moral aspects of adaptive behavior of artificial agents

This article describes a model of a social agent, whose behavior can be stated in terms of basic moral mechanisms and norms. Morality is considered here as a flexible adaptive mechanism that allows agents to vary behavior depending on the environment conditions. The control system of the social agent is based on the &amp;quot;emotion-requirement&amp;quot; architecture. Together with the mechanisms of imitative behavior and the identification of other observable agents with the subjective &amp;quot;Me&amp;quot; concept, this architecture allows to interpret the agent&amp;apos;s behavior in terms of empathy, sympathy, and friend-foe relationships. Experiments with this model are described, the main variable parameter of which was the tendency to sympathy. The objective of the experiments was to determine the dependence of the group &amp;quot;well-being&amp;quot; indicators on their altruism. The results obtained are quite consistent with the well-known sociological conclusions, which made it possible to say that the proposed behavioral models and architecture of agents are adequate to intuitive ideas about the role and essence of morality. Thus, the possibility of transition in this area from abstract humanitarian reasoning to constructive schemes and models of adaptive behavior of artificial agents was demonstrated.

Occupant behaviour in mixed-mode office buildings in a subtropical climate: Beyond typical models of adaptive actions

Recent years have seen considerable interest amongst building professionals and researchers in describing occupant behaviour. This has led to the development of models of adaptive behaviour to improve the accuracy and realism of building energy and indoor environment simulations. The objective of this study was to go beyond the typical behaviour models for building simulation and suggest models for other adaptive actions, such as changing clothes or ingestion of hot or cold beverages. The scope of the study is mixed-mode buildings, which alternate the air-conditioning operation and the opening of windows according to outdoor conditions and occupant preferences. A field study on occupant thermal comfort and behaviour in office buildings was performed in a subtropical climate in Brazil. Simple and multiple logistic models and mixed effects logistic models of adaptive actions were used to analyse key variables based on the results of the field campaign. Occupant clothing behaviour was affected by thermal sensation, clothing insulation and indoor operative temperature. The action of drinking a hot beverage was affected by occupant thermal sensation and the indoor operative temperature, while the cold drink intake was linked to both indoor and outdoor thermal conditions. Our analysis of windows and air-conditioning operation may be used by researchers and practitioners as input for simulations of mixed-mode offices, which can improve energy use predictions.

A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task.

Adaptive behavior emerges through a dynamic interaction between cognitive agents and changing environmental demands. The investigation of information processing underlying adaptive behavior relies on controlled experimental settings in which individuals are asked to accomplish demanding tasks whereby a hidden regularity or an abstract rule has to be learned dynamically. Although performance in such tasks is considered as a proxy for measuring high-level cognitive processes, the standard approach consists in summarizing observed response patterns by simple heuristic scoring measures. With this work, we propose and validate a new computational Bayesian model accounting for individual performance in the Wisconsin Card Sorting Test (WCST), a renowned clinical tool to measure set-shifting and deficient inhibitory processes on the basis of environmental feedback. We formalize the interaction between the task’s structure, the received feedback, and the agent’s behavior by building a model of the information processing mechanisms used to infer the hidden rules of the task environment. Furthermore, we embed the new model within the mathematical framework of the Bayesian Brain Theory (BBT), according to which beliefs about hidden environmental states are dynamically updated following the logic of Bayesian inference. Our computational model maps distinct cognitive processes into separable, neurobiologically plausible, information-theoretic constructs underlying observed response patterns. We assess model identification and expressiveness in accounting for meaningful human performance through extensive simulation studies. We then validate the model on real behavioral data in order to highlight the utility of the proposed model in recovering cognitive dynamics at an individual level. We highlight the potentials of our model in decomposing adaptive behavior in the WCST into several information-theoretic metrics revealing the trial-by-trial unfolding of information processing by focusing on two exemplary individuals whose behavior is examined in depth. Finally, we focus on the theoretical implications of our computational model by discussing the mapping between BBT constructs and functional neuroanatomical correlates of task performance. We further discuss the empirical benefit of recovering the assumed dynamics of information processing for both clinical and research practices, such as neurological assessment and model-based neuroscience.

Imprecise neural computations as a source of adaptive behaviour in volatile environments.

In everyday life, humans face environments that feature uncertain and volatile or changing situations. Efficient adaptive behaviour must take into account uncertainty and volatility. Previous models of adaptive behaviour involve inferences about volatility that rely on complex and often intractable computations. Because such computations are presumably implausible biologically, it is unclear how humans develop efficient adaptive behaviours in such environments. Here, we demonstrate a counterintuitive result: simple, low-level inferences confined to uncertainty can produce near-optimal adaptive behaviour, regardless of the environmental volatility, assuming imprecisions in computation that conform to the psychophysical Weber law. We further show empirically that this Weber-imprecision model explains human behaviour in volatile environments better than optimal adaptive models that rely on high-level inferences about volatility, even when considering biologically plausible approximations of such models, as well as non-inferential models like adaptive reinforcement learning.

On tailoring and hand‐me‐downs

In Schneider et al,1 the authors would like to correct the last two paragraphs on page 428 as: In the second article, Chen, Ou, Wang, Peng, and Davison (2020) explore why, despite substantial investments in Customer Relationship Management (CRM) systems, companies continue to experience pain (such as low utilization and no integration with business processes) rather than profit. The authors examine frontline employees’ interaction with CRM systems by investigating their adaptive behavior. Based on coping theory, the authors develop a CRM system-driven adaptive behavior model that explains how CRM systems facilitate individual employees’ service performance by enabling adaptive behavior during their service encounters. Multisourced data from a financial company in China largely support the research model, showing that employees’ post-adoption of CRM systems (routinization and infusion of use) enables interpersonal and offering adaptive behaviors, which in turn enhance service performance. In the third article, Chen, Zhang, Gong, Lee, and Wang (2020) note that problematic smartphone game use has become an increasing concern with the proliferation of personal smartphone usage. As problematic behaviors cause harmful consequences, a key issue that deserves research attention is how to motivate recovery behavior. However, the drivers that prompt players to attempt to decrease their problematic use are largely unreported. The authors propose a recovery strategy by suggesting how players can be motivated to decrease their usage of smartphone games. The authors develop and test a research model with a scenario-based survey method. They provide insights into how gaming habits can be decreased, thus contributing to the development of an effective recovery strategy. REFERENCE 1. Schneider, C., Hardin, A. and Davison, R.M. (2020), On tailoring and hand-me-downs. Info Systems J, 30:427-430. doi:10.1111/isj.12279

A geometric model for following the intended trajectories from the pursuer to the target

This article describes algorithms simulating the trajectory of the intended movement from the pursuer to the target at any given time. In the previously considered models of adaptive behavior, the pursuer and the target analyzed the coordinates of the opponent in the game and made an automatic decision where to move. In the models described in this article the behavior of the pursuer is determined by the location of the intersection point of the radius circle equal to the discrete step of the target and the assumed simulated trajectory at a given time.

How uncertainty begets hope: A model of adaptive and maladaptive seeking behavior.

The "incentive hope" model creatively explains hoarding and fat accumulation by foragers under uncertainty and food seeking when food cues are present but food is not. The model has difficulty explaining why animals driven by cues fare better than animals driven by food reward itself, why human obesity exists when food is abundant, and why people enjoy gambling and care about winning.

Growth mixture models of adaptive behavior in adolescents with autism spectrum disorder.

This study examined growth trajectories of teacher-reported adaptive behavior in a diverse sample of adolescents with autism spectrum disorder. The participants were 244 adolescents between the ages of 14 and 21 years who were assessed at up to four time points across two and a half years of high school. Demographic variables (age, sex, race, maternal education), phenotypic characteristics (intelligence quotient, autism severity) and school factors (location of the school, school quality) were collected. Growth mixture modeling was used to identify distinct classes of growth trajectories in communication, daily living skills, and socialization domains of adaptive behavior. Two distinct classes were identified for each domain. The first class had moderately low adaptive behavior scores and demonstrated growth of adaptive behavior over time and the second class had low adaptive behavior scores and did not demonstrate change over time. Adolescents within the moderately low adaptive behavior classes were younger at enrollment in the study, had higher IQs, and lower autism symptom severity. Logistic regressions were performed, and aspects of school quality predicted the likelihood of being in the moderately low classes above and beyond autism symptoms.

On repeated stackelberg security game with the cooperative human behavior model for wildlife protection

Inspired by successful deployments of Stackelberg Security Game in real life, researchers are working hard to optimize the game models to make them more practical. Recent security game work on wildlife protection makes a step forward by taking the possible cooperation among attackers into consideration. However, it models attackers to have complete rationality, which is not always possible in practice given they are human beings. We aim to tackle attackers' bounded rationality in the complicated, cooperation-enabled and multi-round security game for wildlife protection. Specifically, we construct a repeated Stackelberg game, and propose a novel adaptive human behavior model for attackers based on it. Despite generating defender's optimal strategy requires to solve a non-linear and non-convex optimization problem, we are able to propose an efficient algorithm that approximately solve this problem. We perform extensive real-life experiments, and results from over 25,000 game plays show our solution effectively helps the defender to deal with attackers who might cooperate.

Adaptive Behavior Modeling in Logistic Systems with Agents and Dynamic Graphs

Inside a logistic system, actors of the logistics have to interact to manage a coherent flow of goods. They also must deal with the constraints of their environment. The article’s first goal is to study how macro properties (such as global performance) emerge from the dynamic and local behaviors of actors and the structure of the territory. The second goal is to understand which local parameters affect these macro properties. A multi-scale approach made of an agent-based model coupled with dynamic graphs describes the system’s components, including actors and the transportation network. Adaptive behaviors are implemented in this model (with data about the Seine axis) to highlight the system’s dynamics. Agent strategies are evolving according to traffic dynamics and disruptions. This logistic system simulator has the capacity to exhibit large-scale evolution of territorial behavior and efficiency face to various scenarios of local agent behaviors.

Species traits and network structure predict the success and impacts of pollinator invasions

Species invasions constitute a major and poorly understood threat to plant–pollinator systems. General theory predicting which factors drive species invasion success and subsequent effects on native ecosystems is particularly lacking. We address this problem using a consumer–resource model of adaptive behavior and population dynamics to evaluate the invasion success of alien pollinators into plant–pollinator networks and their impact on native species. We introduce pollinator species with different foraging traits into network models with different levels of species richness, connectance, and nestedness. Among 31 factors tested, including network and alien properties, we find that aliens with high foraging efficiency are the most successful invaders. Networks exhibiting high alien–native diet overlap, fraction of alien-visited plant species, most-generalist plant connectivity, and number of specialist pollinator species are the most impacted by invaders. Our results mimic several disparate observations conducted in the field and potentially elucidate the mechanisms responsible for their variability.