Complex Behavioral Patterns Research Articles

Using tenets from the Bhagavad Gita in counselling diaspora Hindu older adults: Impact on stress mitigation and improving quality of life

AbstractThis article reports a study on the impact of a Bhagavad Gita (BG) text‐based counselling programme for long‐term immigrant diaspora Hindu older adults in the US and UK. Compared with the journal writing sessions, participants of the BG lessons reported lower stress and better quality of life post‐test (p ≤ 0.025; Hedges' g = −0.65–0.36). Results of the latent class analyses highlighted complex behaviour patterns in the post‐test dataset and foregrounded six classes/subgroups of participants likely to gain most from the BG programme: older adult females, diaspora Hindu older adults with postgraduate‐professional qualifications, widowed older adults, those living alone or with other kith‐kin, those with above threshold programme compliance (defined as those who attended 13–24 (>50%) BG lessons, and completed 13–24 (>50%) BG homework lessons). Overall, the results of the study endorse the connection between age and religiosity and support a BG text‐based counselling programme as a workable strategy for long‐term immigrant diaspora Hindu older adults. The BG text provides a firm ethnoculturally grounded counselling approach to work with diaspora Hindu older adult clients, taking cognisance of their intersectional realities. It may help them cope with the dynamics of the modern, traditional, and continuously shifting realities in a foreign milieu.

Towards Building a Visual Behaviour Analysis Pipeline for Suicide Detection and Prevention.

Understanding human behaviours through video analysis has seen significant research progress in recent years with the advancement of deep learning. This topic is of great importance to the next generation of intelligent visual surveillance systems which are capable of real-time detection and analysis of human behaviours. One important application is to automatically monitor and detect individuals who are in crisis at suicide hotspots to facilitate early intervention and prevention. However, there is still a significant gap between research in human action recognition and visual video processing in general, and their application to monitor hotspots for suicide prevention. While complex backgrounds, non-rigid movements of pedestrians and limitations of surveillance cameras and multi-task requirements for a surveillance system all pose challenges to the development of such systems, a further challenge is the detection of crisis behaviours before a suicide attempt is made, and there is a paucity of datasets in this area due to privacy and confidentiality issues. Most relevant research only applies to detecting suicides such as hangings or jumps from bridges, providing no potential for early prevention. In this research, these problems are addressed by proposing a new modular design for an intelligent visual processing pipeline that is capable of pedestrian detection, tracking, pose estimation and recognition of both normal actions and high risk behavioural cues that are important indicators of a suicide attempt. Specifically, based on the key finding that human body gestures can be used for the detection of social signals that potentially precede a suicide attempt, a new 2D skeleton-based action recognition algorithm is proposed. By using a two-branch network that takes advantage of three types of skeleton-based features extracted from a sequence of frames and a stacked LSTM structure, the model predicts the action label at each time step. It achieved good performance on both the public dataset JHMDB and a smaller private CCTV footage collection on action recognition. Moreover, a logical layer, which uses knowledge from a human coding study to recognise pre-suicide behaviour indicators, has been built on top of the action recognition module to compensate for the small dataset size. It enables complex behaviour patterns to be recognised even from smaller datasets. The whole pipeline has been tested in a real-world application of suicide prevention using simulated footage from a surveillance system installed at a suicide hotspot, and preliminary results confirm its effectiveness at capturing crisis behaviour indicators for early detection and prevention of suicide.

Abstract- Poster

INTRODUCTION:Temporal lobe epilepsy (TLE) begins in temporal lobes of brain, which process emotions, short-term memory, urges such as hunger, thirst, fight-or-flight and sexual arousal. Risk factors for the development of psychopathology in TLE include poorly-controlled seizures, multiple discharges on EEG, structural lesions and psycho-social factors. Childhood onset affects the development of personality and social functioning adversely.CASE:A 17 years old female was diagnosed with TLE 3 years ago following an episode of disorganized behavior, aggression and confusion. Neurologist was consulted after encountering many bad experiences with faith-healers. EEG suggested left fronto-temporal epileptogenic focus. She showed improvement with Phenytoin(300mg) but eventually became noncompliant. She developed repetitive and intrusive obscene thoughts and started to count numbers to relieve the associated anxiety. She was started on Sertraline(50mg) and resumed Phenytoin. Seizures decreased but patient developed aggression, suicidal gestures and had one impulsive suicidal attempt which required hospitalization. In her recent admission with us for aggression, obsessions and suicidal ideation, she showed moderate improvement with Sodium Valproate(600mg) and Fluoxetine(20mg).DISCUSSION:Patients with TLE showed higher incidence of psychiatric disorders including mood disorders, Obsessive compulsive disorder, Generalized anxiety disorder, self-injurious behaviors and personality changes. Complex behavioral patterns seen with epilepsy may arise from the epilepsy itself, as a postictal phenomenon or from a comorbid psychiatric condition.CONCLUSION:Further studies are needed to understand biological underpinnings of these psychiatric symptoms.

Stochastic resonance in asymmetric bistable coupled network systems driven by Gaussian colored noise

In this work studied is the synergistic effect of asymmetric bistable coupled network systems under the action of Gaussian colored noise and periodic signal. The system is a network model consisting of a large number of oscillators. The interaction and change between individuals produce complex nonlinear behavior patterns. For further research, firstly, the original N-dimensional system is reduced and approximated by using the mean field theory, the unified colored noise approximation theory and the equivalent nonlinearization method. Secondly, the Langevin equation of simplified model is obtained through the slaving principle by using the two-state model theory to derive the theoretical expression of signal-to-noise ratio. It is found that the system produces the phenomenon of scale stochastic resonance. Finally, the effects of Gaussian color noise parameters, system parameters and periodic signal parameters on the stochastic resonance behavior of asymmetric coupled network systems are discussed. The results show that the increase of Gaussian colored noise correlation time and noise intensity can promote the scale stochastic resonance phenomenon; selecting appropriate coupling coefficient can achieve the optimal stochastic resonance effect. And the stochastic resonance phenomenon of the system driven by the Gaussian colored noise and the Gaussian white noise, respectively, are analyzed and compared with each other. Research result shows that Gaussian colored noise is more conducive to enhancing stochastic resonance phenomenon.

ІДЕНТИФІКАЦІЇ ПОТЕНЦІЙНОГО СПОЖИВАЧА ПРОДУКЦІЇ РИНКУ ЕЛЕКТРОННОЇ КОМЕРЦІЇ МЕТОДОМ ГРАДІЄНТНОГО БУСТІНГУ

E-commerce is an integral part of the developed economy in the country. Small and large businesses can sell their products or services online, meeting the needs of consumers anywhere and anytime. The development of e-commerce is impossible without the knowledge of consumer behavior. Data has become one of the world's most valuable resources due to the rapid digital transformation of global industries. Collecting customer data has become a top priority for businesses. As more and more advanced technologies are developed to collect and analyze customer data, more companies are able to contextualize, retrieve and monetize information from them. Knowing why people buy, companies can grow more effectively in e-commerce and do so strategically, knowing what next steps to take. From consumer behavior to predictive analytics, companies regularly collect, store and analyze large amounts of quantitative and qualitative data about their consumer base on a daily basis. E-commerce services are one of the areas where new data is collected every day. Therefore, it seems necessary to use data analysis methods in this area. User interactions with e-commerce platforms are complex patterns of behavior that, if analyzed, can enable businesses to understand consumer needs. Consumer buying behavior is influenced by many factors, and different consumer demands lead to large differences in consumer buying behavior. To predict consumer buying behavior it is necessary to determine the hidden characteristics of data in the array of information left by users on the e-commerce platform, and then to determine the desire of future users to buy on the e-commerce platform. The article considers an example of application of the gradient boosting algorithm for consumer identification in the e-commerce market.

Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches.

Large and densely sampled sensor datasets can contain a range of complex stochastic structures that are difficult to accommodate in conventional linear models. This can confound attempts to build a more complete picture of an animal’s behavior by aggregating information across multiple asynchronous sensor platforms. The Livestock Informatics Toolkit (LIT) has been developed in R to better facilitate knowledge discovery of complex behavioral patterns across Precision Livestock Farming (PLF) data streams using novel unsupervised machine learning and information theoretic approaches. The utility of this analytical pipeline is demonstrated using data from a 6-month feed trial conducted on a closed herd of 185 mix-parity organic dairy cows. Insights into the tradeoffs between behaviors in time budgets acquired from ear tag accelerometer records were improved by augmenting conventional hierarchical clustering techniques with a novel simulation-based approach designed to mimic the complex error structures of sensor data. These simulations were then repurposed to compress the information in this data stream into robust empirically-determined encodings using a novel pruning algorithm. Nonparametric and semiparametric tests using mutual and pointwise information subsequently revealed complex nonlinear associations between encodings of overall time budgets and the order that cows entered the parlor to be milked.

High Encephalization in a Fossil Rorqual Illuminates Baleen Whale Brain Evolution

Baleen whales are considered underencephalized mammals due to their reduced brain size with respect to their body size (encephalization quotient [EQ] << 1). Despite their low EQ, mysticetes exhibit complex behavioral patterns in terms of motor abilities, vocal repertoire, and cultural learning. Very scarce information is available about the morphological evolution of the brain in this group; this makes it difficult to investigate the historical changes in brain shape and size in order to relate the origin of the complex mysticete behavioral repertoire to the evolution of specific neural substrates. Here, the first description of the virtual endocast of a fossil balaenopterid species, Marzanoptera tersillae from the Italian Pliocene, reveals an EQ of around 3, which is exceptional for baleen whales. The endocast showed a morphologically different organization of the brain in this fossil whale as the cerebral hemispheres are anteroposteriorly shortened, the cerebellum lacks the posteromedial expansion of the cerebellar hemispheres, and the cerebellar vermis is unusually reduced. The comparative reductions of the cerebral and cerebellar hemispheres suggest that the motor behavior of M. tersillae probably was less sophisticated than that exhibited by the extant rorqual and humpback species. The presence of an EQ value in this fossil species that is around 10 times higher than that of extant mysticetes opens new questions about brain evolution and provides new, invaluable information about the evolutionary path of morphological and size change in the brain of baleen whales.

Mental health and substance use patterns associated with lifetime suicide attempt, incarceration, and homelessness: A latent class analysis of a nationally representative sample of U.S. veterans.

A substantial proportion of U.S. military veterans experience mental health and/or substance use disorders. As public awareness of these difficulties rises, so too does interest in the relation between veteran behavioral health and high-risk events, namely suicide attempts, incarceration, and homelessness. Using latent class analysis and a large, nationally representative sample of community veterans, the current study examined common patterns of veteran behavioral health disorders and the associations between these patterns and high-risk events. Results suggest four classes of veterans, including a "healthy" class (comprising 69% of the total sample), a "substance use disorder" class (16%), a "personality disorder-substance use disorder" class (8%), and a "depressive disorder" class (7%). Veterans in the healthy class had the lowest rates of suicide attempt (2%), incarceration (14%), and homelessness (3%), whereas veterans in the personality disorder-substance use disorder class had the highest rates of these events (27%, 45%, and 22%, respectively). Results attest to the importance of investing clinical resources into addressing the needs of veterans experiencing complex behavioral health patterns, particularly personality and substance use disorders. Implications for the health care of veterans are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Simple Physical Interactions Yield Social Self-Organization in Honeybees

Social insect colonies show all characteristics of complex adaptive systems (CAS). Their complex behavioral patterns arise from social interactions that are based on the individuals’ reactions to and interactions with environmental stimuli. We study here how social and environmental factors modulate and bias the collective thermotaxis of young honeybees. Therefore, we record their collective decision-making in a series of laboratory experiments and derived a mathematical model of the collective decision-making in young bees from our empirical observations. This model uses only one free parameter that combines the ultimate effects of several aspects of the microscopic individual behavioral mechanisms, such as motion behavior, sensory range, or contact detection, into one single coefficient. We call this coefficient the “social factor.” Our model is capable of capturing the observed aggregation patterns from our empiric experiments with static environments and of predicting the emergent swarm-intelligent behavior of the system in dynamic environments. Besides the fundamental research aspect in studying CAS, our model enables us to predict the effects of a physical stimulus onto the macroscopic collective decision-making that affects several crucial prerequisites for efficient and effective brood production and population growth in honeybee colonies.

Development of an Ontology-Based Visual Approach for Property Data Analytics

Real estate is a complex market that consists of many layers of social, financial, and economic data, including but not limited to price, rental, location, mortgage, demographic and housing supply data. The sheer number of real estate properties around the world means that property transactions produce an extraordinary amount of data that is increasing exponentially. Most of the data are presented through thousands of rows on a spreadsheet or described in long paragraphs that are difficult to understand. The emergent data visualization techniques are intended to allow data to be processed and analytics to be displayed visually to enable an understanding of complex information and the identification of new patterns from the data. However, not all visualization techniques can achieve such a thing. Most techniques are able to display only visual low-dimensional data. This paper introduces an ontology visualisation methodology to explore the ontologies of property data behaviour for multidimensional data. The visualisation combines real estate data statistical analysis with several high dimensional data visualisation techniques, including parallel coordinates and stacked area charts. By using six residential suburbs in Sydney as a demonstration, we find that the developed data visualisation methodology can be applied effectively and efficiently to analyse complex real estate market behaviour patterns.

Begonia-A Two-Photon Imaging Analysis Pipeline for Astrocytic Ca2+ Signals.

Imaging the intact brain of awake behaving mice without the dampening effects of anesthesia, has revealed an exceedingly rich repertoire of astrocytic Ca2+ signals. Analyzing and interpreting such complex signals pose many challenges. Traditional analyses of fluorescent changes typically rely on manually outlined static region-of-interests, but such analyses fail to capture the intricate spatiotemporal patterns of astrocytic Ca2+ dynamics. Moreover, all astrocytic Ca2+ imaging data obtained from awake behaving mice need to be interpreted in light of the complex behavioral patterns of the animal. Hence processing multimodal data, including animal behavior metrics, stimulation timings, and electrophysiological signals is needed to interpret astrocytic Ca2+ signals. Managing and incorporating these data types into a coherent analysis pipeline is challenging and time-consuming, especially if research protocols change or new data types are added. Here, we introduce Begonia, a MATLAB-based data management and analysis toolbox tailored for the analyses of astrocytic Ca2+ signals in conjunction with behavioral data. The analysis suite includes an automatic, event-based algorithm with few input parameters that can capture a high level of spatiotemporal complexity of astrocytic Ca2+ signals. The toolbox enables the experimentalist to quantify astrocytic Ca2+ signals in a precise and unbiased way and combine them with other types of time series data.

AuthCODE: A privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning

The authentication field is evolving towards mechanisms able to keep users continuously authenticated without the necessity of remembering or possessing authentication credentials. While existing continuous authentication systems have demonstrated their suitability for single-device scenarios, the Internet of Things and next generation of mobile networks (5G) are enabling novel multi-device scenarios -- such as Smart Offices -- where continuous authentication is still an open challenge. The paper at hand, proposes an AI-based, privacy-preserving and multi-device continuous authentication architecture called AuthCODE. A realistic Smart Office scenario with several users, interacting with their mobile devices and personal computer, has been used to create a set of single- and multi-device behavioural datasets and validate AuthCODE. A pool of experiments with machine and deep learning classifiers measured the impact of time in authentication accuracy and improved the results of single-device approaches by considering multi-device behaviour profiles. The f1-score average reached for XGBoost on multi-device profiles based on 1-minute windows was 99.33%, while the best performance achieved for single devices was lower than 97.39%. The inclusion of temporal information in the form of vector sequences classified by a Long-Short Term Memory Network, allowed the identification of additional complex behaviour patterns associated to each user, resulting in an average f1-score of 99.02% on identification of long-term behaviours.

Understanding the dynamics emerging from infodemics: a call to action for interdisciplinary research

Research on infodemics, i.e., the rapid spread of (mis)information related to a hazardous event, such as the COVID-19 pandemic, requires integrating a multiplicity of scientific disciplines. The dynamics emerging from infodemics have the potential to generate complex behavioral patterns. To react appropriately, it is of ultimate importance for the fields of Business and Economics to understand these dynamics. In the short run, they might lead to an adaptation in household spending or to a shift in buying behavior towards online providers. In the long run, changes in investments, consumer behavior, and markets are to be expected. We argue that the dynamics emerge from complex interactions among multiple factors, such as information and misinformation accessible to individuals and the formation and revision of beliefs. (Mis)information accessible to individuals is, amongst others, affected by algorithms specifically designed to provide personalized information, while automated fact-checking algorithms can help reduce the amount of circulating misinformation. The formation and revision of individual (and probably false) beliefs and individual fact-checking and interpretation of information are heavily affected by linguistic patterns inherent to information during pandemics and infodemics and further factors, such as affect, intuition, and motives. We argue that, to get a deep(er) understanding of the dynamics emerging from infodemics, the fields of Business and Economics should integrate the perspectives of Computer Science and Information Systems, (Computational) Linguistics, and Cognitive Science into the wider context of economic systems (e.g., organizations, markets or industries) and propose a way to do so. As research on infodemics is a strongly interdisciplinary field and the integration of the above-mentioned disciplines is a first step towards a holistic approach, we conclude with a call to action which should encourage researchers to collaborate across scientific disciplines and unfold collective creativity, which will substantially advance research on infodemics.

Kinetic Analysis of Transient Intermediates in the Mechanism of Prenyl-Flavin-Dependent Ferulic Acid Decarboxylase.

Ferulic acid decarboxylase catalyzes the decarboxylation of various substituted phenylacrylic acids to their corresponding styrene derivatives and CO2 using the recently discovered cofactor prenylated FMN (prFMN). The mechanism involves an unusual 1,3-dipolar cycloaddition reaction between prFMN and the substrate to generate a cycloadduct capable of undergoing decarboxylation. Using native mass spectrometry, we show the enzyme forms a stable prFMN-styrene cycloadduct that accumulates on the enzyme during turnover. Pre-steady state kinetic analysis of the reaction using ultraviolet-visible stopped-flow spectroscopy reveals a complex pattern of kinetic behavior, best described by a half-of-sites model involving negative cooperativity between the two subunits of the dimeric enzyme. For the reactive site, the cycloadduct of prFMN with phenylacylic acid is formed with a kapp of 131 s-1. This intermediate converts to the prFMN-styrene cycloadduct with a kapp of 75 s-1. Cycloelimination of the prFMN-styrene cycloadduct to generate styrene and free enzyme appears to determine kcat for the overall reaction, which is 11.3 s-1.

The Body I Live in. Perceptions and Meanings of Body Dissatisfaction in Young Transgender Adults: A Qualitative Study.

Body dissatisfaction in individuals with Gender Incongruence (GI) represents a primary source of suffering. Several studies have highlighted how this suffering has psychological, physical, and biological implications. This work aims to explore experiences related to body dissatisfaction and investigate the issues associated with living in a body perceived as incongruent for individuals with GI. Thirty-six individuals, aged between 18 and 30 years old and at stage T0 of hormone treatment, participated in the study. Body dissatisfaction and experiences related to it were investigated using the Clinical Diagnostic Interview. The Consensual Qualitative Research methodology was applied to the transcripts of the interviews. Several themes emerged: experiences with GI development, experiences with puberty and bodily changes, perception of one’s body, psychological problems and complex behavioral patterns related to body dissatisfaction. Results pointed out the complexity implied in the relationship with one’s body for individuals with GI, highlighting specific aspects of body dissatisfaction among these individuals (e.g., eating disorders, sexual difficulties, social withdrawal). This study underlines the need for a deeper understanding of some aspects of GI to better define guidelines for a correct assessment of it. In this way it will be easier to avoid negative outcomes for the psychological and general health of transgender people.

A new metaheuristic approach based on agent systems principles

Abstract Agent-based modeling is a relatively new approach to model complex systems composed of agents whose behavior is described using simple rules. As a consequence of the agent interactions emerges a complex global behavioral pattern not explicitly programmed. In the last decade, an increasing number of metaheuristic techniques have been reported in the literature where authors claim their novelty and their abilities to perform as powerful optimization methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. The idea behind the design of many metaheuristic methods is to configure a recycled set of rules that has demonstrated to be successful in previous approaches for producing new optimization schemes. Such common rules have been designed without considering the final global result obtained by the individual interactions. On the other hand, agent-based systems provide a solid theory and a set of consistent models that allow characterizing global behavioral patterns produced by the collective interaction of the individuals from a set of simple rules. Under this perspective, several agent-based concepts and models that generate very complex global search behaviors can be used to produce or improve efficient optimization algorithms. In this paper, a new metaheuristic algorithm based on agent systems principles is presented. The proposed method is based on the agent-based model known as “Heroes and Cowards”. This model involves a small set of rules to produce two emergent global patterns that can be considered in terms of the metaheuristic literature as exploration and exploitation stages. To evaluate its performance, the proposed algorithm has been tested in a set of representative benchmark functions, including multimodal, unimodal, and hybrid benchmark formulations. The competitive results demonstrate the promising association between both paradigms.

First large‐scale provenance study of pigments reveals new complex behavioural patterns during the Upper Palaeolithic of south‐western Germany

The use of red iron‐based earth pigments, or ochre, is a key component of early symbolic behaviours for anatomically modern humans and possibly Neanderthals. We present the first ochre provenance study in Central Europe showing long‐term selection strategies by inhabitants of cave sites in south‐western Germany during the Upper Palaeolithic (43–14.5 ka). Ochre artefacts from Hohle Fels, Geißenklösterle and Vogelherd, and local and extra‐local sources, were investigated using neutron activation analysis (NAA), X‐ray diffraction (XRD) and scanning electron microscopy (SEM). The results show that local ochre sources were continuously and systematically accessed for c.29 500 years, with periodic events of long‐distance (about &gt; 300 km) ochre acquisition during the Aurignacian (c.35–43 ka), suggesting higher mobility than previously suspected. The results reveal previously unknown long‐term, complex spatio‐temporal behavioural patterns during the earliest presence of Homo sapiens in Europe.

A Practical Guide to Using Time-and-Motion Methods to Monitor Compliance With Hand Hygiene Guidelines: Experience From Tanzanian Labor Wards.

Good-quality evidence on hand hygiene compliance among birth attendants in low-resource labor wards is limited. The World Health Organization Hand Hygiene Observation Form is widely used for directly observing behaviors, but it does not support capturing complex patterns of behavior. We developed the HANDS at Birth tool for direct observational studies of complex patterns of hand rubbing/washing, glove use, recontamination, and their determinants among birth attendants. Understanding these behaviors is particularly critical in wards with variable patient volumes or unpredictable patient complications, such as emergency departments, operating wards, or triage and isolation wards during epidemics. Here we provide detailed information on the design and implementation of the HANDS at Birth tool, with a particular focus on low-resource settings. We developed the HANDS at Birth tool from available guidelines, unstructured observation, and iterative refinement based on consultation with collaborators and pilot results. We designed the tool with WOMBAT software, which supports collecting multidimensional time-and-motion data. Our analysis of the tool's performance centered on interobserver agreement and convergent validity and the implications of the data structure for data analysis. The HANDS at Birth tool encompasses various hand actions and context-relevant information. Hand actions include procedures relevant during labor and delivery; hand hygiene or glove actions; and other types of touch. During field implementation, we used the tool for continuous observation of the birth attendant. Interobserver agreement was good (kappa range: 0.7-0.9), and the tool showed convergent validity. Using the HANDS at Birth tool is a feasible way to obtain useful information about compliance with hand hygiene procedures. The tool could be used after simple training and allows for collection of reliable information about the complex pattern of hygiene behaviors. Future studies should explore using this tool to observe behavior in labor wards in other settings and in other types of wards.

Convergence of multiple fields on a relational reasoning approach to cognition.

The current paper briefly outlines how researchers from multiple sub-fields of cognitive science have independently converged upon a relational account of language and cognition. However, most of this research from the cognitive sciences has focused on the description and prediction of cognitive/linguistic outcomes, rather than on influencing them per se. In particular, there is general consensus from cognitive science that cognitive training programs are ineffective. On the other hand, behavioral selection by consequences (i.e., operant psychology) is one of the most robust findings in psychological science, and is a principle commonly employed to influence behavior across a range of settings. We provide an introduction to Relational Frame Theory (RFT) as a form of advanced operant psychology, and describe how relatively complex patterns of behavior can be shaped up by applying targeted schedules of reinforcement to more primitive relational responses, drawing on examples from contemporary cognitive training literature and experimental behavior analysis.

Exploratory agent-based model to understand migration scenarios: a study from the Indian Himalayan Region, Uttarakhand

ABSTRACT Migration is a complex behavioural pattern which is shaped by cross-scale variables and heuristic rules. This article captures the complexity and dynamic behaviour of migration in Tehri Garhwal district of Uttarakhand using agent-based modelling (ABM). Scenarios considering different starting points were developed to understand variables influencing migration. Migration is governed not only by intrinsic factors, but also by extrinsic influences. Exploratory ABM techniques were used to validate the hypothesis assumed to explain migration behaviour in the study area. The results show that migration cannot be steered with policies focused only on economic perspectives.