Coastal socio-ecological systems face unprecedented challenges due to climate change, with impacts encompassing long-term, chronic changes and short-term extreme events. These events will impact society in many ways and prompt human responses that are extremely challenging to predict. This dissertation employs complex systems methods of agent-based modeling and machine learning to simulate the interactions between climatic stressors such as increased flooding and extreme weather and socio-economic aspects of coastal human systems. Escalating sea-level rise and intensified flooding has the potential to prompt relocation from flood-prone coastal areas. This can reduce flood exposure but also disconnect people from their homes and communities, sever longstanding social ties, and lower the tax base leading to difficulties in providing government services. Chapter 2 demonstrates a stochastic agent-based model to simulate human relocation influenced by flooding events, particularly focusing on the responses of rural and urban communities in coastal Virginia and Maryland. The findings indicate that a stochastic, bottom-up social system simulator is able to replicate top-down population projections and provide a baseline for assessing the impact of increasingly intense flooding. Chapter 3 leverages this model to assess how incorporating heterogeneity in relocation decisions across socio-economic groups impacts flood-induced relocation patterns. The results demonstrate how this heterogeneity leads to a decrease in low-income households, yet a rise in the proportion of elderly individuals in flood-prone regions by the end of the simulation period. Flood-prone areas also exhibit distinct income clusters at the end of simulation time horizon compared to simulations with a homogenous relocation likelihood. Lastly, Chapter 4 explores relationships between extreme weather and agricultural losses in the Delmarva Peninsula. Existing research on climatic impacts to agriculture largely focuses on changes to major crop yields, providing limited insights into impacts on diverse regional agricultural systems where human management and adaptation play a large role. By comparing various multistep modeling configurations and machine learning techniques, this work demonstrates that machine learning methods can accurately simulate and predict agricultural losses across the complex agricultural landscape that exists on the Delmarva peninsula. The multistep configurations developed in this work are able to address data imbalance and improve models' capacity to classify and estimate damage occurrence, which depends on multiple geographical, seasonal, and climatic factors. Collectively, this work demonstrates the potential for advanced modeling techniques to accurately replicate and simulate the impacts of climate on complex socio-ecological systems, providing insights that can ultimately support coastal adaptation. / Doctor of Philosophy / Coastal areas are facing increasing challenges from climate change, including rising sea levels and extreme weather conditions. This dissertation explores socio-economic consequences of these adverse environmental changes for coastal communities. Disruptive repetitive flooding due to exacerbated rise in sea levels is one of these consequences that may eventually leave some highly exposed coastal communities no alternative but migrating from their residences. Focusing on coastal Virginia and Maryland, Chapter 2 develops a data-informed model that can simulate individual relocation decisions and assess how they impact population changes and migration patterns. Chapter 3 employs this model to investigate how future changes in sea levels affect diverse socio-economic groups, their relocation decisions, and the resulting collective migration flows in flood-prone areas. We found that considering demographic differences leaves highly flood-prone areas with less low-income households, higher elderly individuals, and more economic clusters compared to simulations where these differences are not accounted for. Chapter 4 uses machine learning models to simulate the economic impact of extreme weather events as another manifestation of climate change on the agriculture in the Delmarva Peninsula. Through data-based modeling techniques, we identify the climatic conditions most responsible for agricultural losses and recognize modeling choices that enhance our predictive ability. Collectively, this dissertation demonstrates how sophisticated modeling techniques can be used to better understand the complex ways in which climate change will impact human society, with the ultimate goal of supporting adaptation strategies that can better address these impacts.

<p dir="ltr">Deep-space extraterrestrial missions require operating, supporting, and maintaining complex habitat systems at light minutes from Earth.</p><p dir="ltr">These habitation systems operate in harsh, unforgiving environments, will be sparsely crewed, and must be more autonomous than current space habitats, as communication delays will severely constrain Earth-based support.</p><p dir="ltr">Long-duration missions, limited knowledge of the extraterrestrial environment, and the need for self-sufficiency make these habitats vulnerable to a wide range of risks and failures, many of which are impossible to premeditate.</p><p dir="ltr">Therefore, it is necessary to design these systems to be resilient to faults and failures, thoughtfully designed to be situationally aware of their operational state and engage control mechanisms that maintain safe operations when migrating towards unsafe regions of operation.</p><p dir="ltr">Resilience-oriented design of such systems requires a holistic systems approach that represents the system's dynamic behavior, its control-oriented behaviors, and the interactions between them as it navigates through regions of safe and unsafe operations.</p><p dir="ltr">Only through this integrated approach can we fully understand how the system will behave under various conditions and design controls to prevent performance loss and ensure resilient operations.</p><p dir="ltr">Systems health management (SHM) is a key component for the resilience-oriented design of extraterrestrial habitats.</p><p dir="ltr">SHM capabilities enable intelligent autonomous control capabilities that can:</p><p dir="ltr">a) sense, diagnose, and isolate the root causes of anomalies,</p><p dir="ltr">b) predict how the system's behavior may evolve, and</p><p dir="ltr">c) select and execute recovery actions to restore system performance when appropriate.</p><p dir="ltr">Modern SHM technologies increasingly rely on intelligent autonomous control capabilities to manage system health and adapt behavior to maintain system performance.</p><p dir="ltr">This is achieved through complex nonlinear informational dependencies and control feedback loops that are difficult to design and verify using traditional risk assessment and resilience engineering methods.</p><p dir="ltr">This research contributes to enhancing the conceptual and preliminary design phases for developing resilient complex systems with embedded intelligent control-oriented behaviors.</p><p dir="ltr">It presents the required systems engineering tools and frameworks, enabling us to study the dynamic behavior of systems as they approach and recover from unsafe operations.</p><p dir="ltr">Further, it demonstrates how these tools and frameworks can quantify and gain insights into system resilience and support engineering decisions.</p><p dir="ltr">The work is contextualized within the broader systems engineering approach for designing complex, resilient extraterrestrial habitation systems.</p>

Network Direct Selling Organisations (NDSOs) exist in more than 50 countries and have more than 74 million members. The most recent statistical information reveals that the vast majority of members do not earn significant income. Criticism of these organisations revolves around the ethicality of consumption, the commercialisation of personal relationships, and the exploitation of unrealistic expectations. This study aims to explore how communication creates networks that sustain an industry of this kind despite the improbability of its existence. The study commences with a description of NDSOs from historical, operational, tactical, and strategic perspectives. Given the broader context created by the global presence of this industry, cybernetics has been selected as a meta-theoretical perspective for the study of communication. The more recent development of second-order cybernetics and social autopoiesis are introduced to communication theory as a field. Niklas Luhmann‟s new social theory of communication is assessed and applied in relation to existing communication theory. New conceptual models are developed to explore communication as the unity of the synthesis of information, utterance, understanding, and expectations as selections that occur both consciously and unconsciously, intentionally and unintentionally. These models indicate the multiplexity of individual and social operationally closed, yet informationally open systems, and they are used here to provide a systemic and coherent alternative to orthodox communication approaches to the study of organisations. The study adopts a constructivist epistemological stance and propounds throughout the necessity of further interdisciplinary collaboration. The study concludes that individuals are composite unities of self-creating systems, and they co-create social systems by self-creating and co-creating meaning. Meaning is described as the continuous virtualisation and actualisation of potentialities that in turn coordinate individual and social systems‟ actions. A communication process flow model is created to provide a theoretical explanation for the existence of NDSOs as self-creating systems. The study aims to show that communication has arguably become the most pervasive discipline as a result of the globally interactive era. It is shown that second-order cybernetics and social autopoiesis raise several further questions to be explored within communication theory as a field. / Communication, first-order cybernetics, second-order cybernetics, Complexity and complex systems, autopoiesis, self-reference, recursivity, operational closure, system boundaries, Network Direct Selling Organisations / Communication / D. Litt. et Phil. (Communication)

During the last decades “Climate Change” has become a vital topic on national and international political agendas. There it is presented as an irrevocable fact of global impact and thus of universal relevance. What has often been neglected are local discourses of marginalized groups and their specific contextualization of “Climate Change” phenomena. The aim of this project, to develop another perspective along these dominant narratives, has resulted in the research question How is social reality reconstructed on the phenomenon of “Climate Change” among the “Emerging Black Farmers” in the Swartland region in Western Cape, South Africa? Taken as an example, “Climate Change Knowledge” is reconstructed through a case study on the information exchange between the NGO Goedgedacht Trust and local small-scale farmers in the post-Apartheid context of on-going political, social, economic and educational transition in South Africa. Applying a constructivist approach, “Climate Change Knowledge” is not understood as an objectively given, but a socially constructed “reality” that is based on the interdependency of socio-economic conditions and individual assets, including language skills and language practice, sets of social norms and values, as well as strategies of knowledge transfer. The data set consists of qualitative data sources, such as application forms and interview material, which are triangulated. The rationale of a multi-layered data analysis includes a discursive perspective as well as linguistic and ethical “side perspectives”. Epistemologically, the thesis is guided by assumptions of complexity theory, framing knowledge around “Climate Change” as a fluid, constantly changing system that is shaped by constant intra- and inter-systemic exchange processes, and characterized by non-linearity, self-organization and representation of its constituents. From this point of departure, a theoretical terminology has been developed, which differentiates between symbols, interrelations, contents and content clusters. These elements are located in a system of spatio-temporal orientation and embedded into a broader (socio-economic) context of “historicity”. Content clusters are remodelled with the help of concept maps. Starting from that, a local perspective on “Climate Change” is developed, adding an experiential notion to the global narratives. The thesis concludes that there is no single reality about “Climate Change” and that the farmers’ “Climate Change Knowledge” highly depends on experiential relativity and spatio-temporal immediacy. Furthermore, analysis has shown that the system’s historicity and social manifestations can be traced in the scope and emphasis of the content clusters discussed. Finally the thesis demonstrates that characteristics of symbols, interconnections and contents range between dichotomies of direct and indirect, predictable versus unpredictable, awareness and negligence or threat and danger, all coexisting and creating a continuum of knowledge production.

Ce mémoire est une mise à jour philosophique du modèle conceptuel défini par Aloïs Riegl dans son ouvrage sur Le culte moderne des monuments. Pour faire cet aggiornamento, nous présentons dans une première partie ce modèle et l'énigme qu'il contient. Dans la deuxième, nous décrivons le paradigme que les premières communautés patrimoniales adoptèrent dans l'Entre-Deux-Guerres pour la résoudre, puis nous introduisons dans la troisième les concepts qui ancrent leur paradigme dans le monde actuel et nous formulons l'énigme qu'il pose maintenant aux professionnels. Historiquement, nous démontrons ainsi la continuité de l'institution patrimoniale, d'A. Riegl à nos jours. En dépouillant les archives de la Commission internationale de Coopération intellectuelle, nous prouvons que l'ONU et l'UNESCO n'ont pas créé les réseaux de conservation que nous connaissons, mais ont hérités de ceux que la SDN et la CICI tissèrent avant-guerre en fédérant les institutions et les associations qui existaient alors. Philosophiquement, nous mettons au jour le fondement ontologique et épistémologique de l'institution patrimoniale en étudiant différents modèles conceptuels. Nous expliquons comment le réalisme structural peut concilier les théories réalistes et constructivistes qui pourraient s'opposer ici et comment l'approche processus permet d'unifier ses secteurs sans nier les différences de nature qui existent entre ses objets. Notre thèse est que l'on peut modéliser le domaine en interconnectant les points de vue de ses acteurs. Pour l'établir, nous répondons à la question de savoir ce qu'est le patrimoine, comment fonctionne son institution et sur quoi se fonde sa conservation. / This doctoral thesis on epistemology of conservation is a philosophical update of the conceptual model defined by Alois Riegl in his book on The Modern Cult of Monuments. The first part presents the model and its riddle in order to perform this aggiornamento. The second part describes the paradigm adopted by the first heritage communities between the two world wars in their attempt to resolve it. The third part introduces the concepts that connect their paradigm with the present world and formulate the riddle challenging current professionals. The author proves the historic continuity of the heritage institution from A. Riegl to our days. By examining the archives of the International Committee on Intellectual Cooperation (ICIC), he demonstrates that the UN and UNESCO didn't create the heritage networks that we know today but that they originate from the networks of institutions and associations organized by the League of Nations and ICIC before World War I. Philosophically, he brings to light the ontological and epistemological foundation of the heritage institutions by studying several conceptual models. He explains how structural realism can reconcile realism with constructivism, even as they seem to be opposite theories, and alson how the processus approach can unify its parts without negating the differences of nature between its objects. His thesis is that almost all of the domain can be modelized by interconnecting the points of view of all its actors. To establish this point, he answer the following questions : what is heritage ? How does its institution function ? What is its conservation funded upon ?

A evolução tecnológica das redes de telecomunicações sem fio permite que organizações de redes mais inteligentes sejam vislumbradas. É possível imaginar um sistema de telefonia formado por dispositivos móveis autônomos que não necessite de nenhuma infraestrutura pré-estabelecida para trocar informações com seus vizinhos, de acordo com o alcance do raio de transmissão. Assim, as informações poderiam ser repassadas de nó em nó, formando uma rede de múltiplos saltos. A ausência de uma entidade central também poderia melhorar a tolerância a falhas do sistema, principalmente por gerar uma redundância de caminhos possíveis entre os nós. Analisamos o desempenho desse sistema em diferentes cenários e a sensibilidade à variação de parâmetros como o raio de transmissão, interferências, a quantidade de nós e número de saltos máximo permitido (TTL), e testamos estratégias de comunicação com raio fixo, raio variável, número de vizinhos mínimo e etc., através de modelagem e simulação baseada em agentes. De maneira geral, a estratégia de transmissão com raio variável apresentou a melhor taxa de mensagens recebidas e a menor média de saltos até o destino, porém com maior nível de energia do sistema. A estratégia de raio fixo apresentou a menor energia total gasta pelo sistema para enviar as mensagens, porém, com uma taxa menor de mensagens recebidas. Além disso, avaliamos que as principais causas de perdas de pacotes estão associadas com o aumento da mobilidade, a redução do TTL e as interferências, sendo que cada uma contribui mais ou menos de acordo com o cenário estudado. / The technological development of Wireless Networks leads to more intelligent networks structures. One can imagine a mobile data system consisting of autonomous mobile devices that do not require any pre-established infrastructure to exchange information one with another, limited mainly by the transmission radius. Thus, data could be forwarded from node to node, forming a multihop network. The absence of a central entity could also improve fault tolerance by allowing redundant paths for nodes to communicate. We analyzed the performance of the system in different scenarios and system behavior regarding parameters variations such as transmission radius, interferences, the number of nodes and maximum allowed number of hops (TTL), and tested communication strategies with fixed radius, variable radius, minimum number of neighbors to transmit, etc., through modeling and simulation-based agents. In general, variable radius strategy had the best rate of incoming messages and the lowest average number of hops to the destination. However it presented the higher level of system energy. In one hand, fixed radius strategy presented the lowest total energy expended by the system to send messages, but, in the other hand, the rate of incoming messages was lower. Furthermore, we discovered the main causes of packet losses are associated with increased mobility, reducing the TTL and interference, each of which contributes more or less in accordance with the scenario.

O aprendizado de máquina consiste de conceitos e técnicas que permitem aos computadores melhorar seu desempenho com a experiência, ou, em outras palavras, aprender com dados. Um dos principais tópicos do aprendizado de máquina é o agrupamento de dados que, como o nome sugere, procura agrupar os dados de acordo com sua similaridade. Apesar de sua definição relativamente simples, o agrupamento é uma tarefa computacionalmente complexa, tornando proibitivo o emprego de algoritmos exaustivos, na busca pela solução ótima do problema. A importância do agrupamento de dados, aliada aos seus desafios, faz desse campo um ambiente de intensa pesquisa. Também a classe de fenômenos naturais conhecida como comportamento coletivo tem despertado muito interesse. Isso decorre da observação de um estado organizado e global que surge espontaneamente das interações locais presentes em grandes grupos de indivíduos, caracterizando, pois, o que se chama auto-organização ou emergência, para ser mais preciso. Os desafios intrínsecos e a relevância do tema vêm motivando sua pesquisa em diversos ramos da ciência e da engenharia. Ao mesmo tempo, técnicas baseadas em comportamento coletivo vêm sendo empregadas em tarefas de aprendizado de máquina, mostrando-se promissoras e ganhando bastante atenção. No presente trabalho, objetivou-se o desenvolvimento de técnicas de agrupamento baseadas em comportamento coletivo. Faz-se cada item do conjunto de dados corresponder a um indivíduo, definem-se as leis de interação local, e então os indivíduos são colocados a interagir entre si, de modo que os padrões que surgem reflitam os padrões originalmente presentes no conjunto de dados. Abordagens baseadas em dinâmica de troca de energia foram propostas. Os dados permanecem fixos em seu espaço de atributos, mas carregam certa informação a energia , a qual é progressivamente trocada entre eles. Os grupos são estabelecidos entre dados que tomam estados de energia semelhantes. Este trabalho abordou também o aprendizado semissupervisionado, cuja tarefa é rotular dados em bases parcialmente rotuladas. Nesse caso, foi adotada uma abordagem baseada na movimentação dos próprios dados pelo espaço de atributos. Procurou-se, durante todo este trabalho, não apenas propor novas técnicas de aprendizado, mas principalmente, por meio de muitas simulações e ilustrações, mostrar como elas se comportam em diferentes cenários, num esforço em mostrar onde reside a vantagem de se utilizar a dinâmica coletiva na concepção dessas técnicas / Machine learning consists of concepts and techniques that enable computers to improve their performance with experience, i.e., enable computers to learn from data. Data clustering (or just clustering) is one of its main topics, which aims to group data according to their similarities. Regardless of its simple definition, clustering is a complex computational task. Its relevance and challenges make this field an environment of intense research. The class of natural phenomena known as collective behavior has also attracted much interest. This is due to the observation that global patterns may spontaneously arise from local interactions among large groups of individuals, what is know as self-organization (or emergence). The challenges and relevance of the subject are encouraging its research in many branches of science and engineering. At the same time, techniques based on collective behavior are being employed in machine learning tasks, showing to be promising. The objective of the present work was to develop clustering techniques based on collective behavior. Each dataset item corresponds to an individual. Once the local interactions are defined, the individuals begin to interact with each other. It is expected that the patterns arising from these interactions match the patterns originally present in the dataset. Approaches based on dynamics of energy exchange have been proposed. The data are kept fixed in their feature space, but they carry some sort of information (the energy), which is progressively exchanged among them. The groups are established among data that take similar energy states. This work has also addressed the semi-supervised learning task, which aims to label data in partially labeled datasets. In this case, it has been proposed an approach based on the motion of the data themselves around the feature space. More than just providing new machine learning techniques, this research has tried to show how the techniques behave in different scenarios, in an effort to show where lies the advantage of using collective dynamics in the design of such techniques

Os sistemas naturais e humanos são considerados sistemas integrados, com interações complexas e de caráter fortemente multidisciplinar. A abordagem da limnologia da paisagem, como um princípio holístico para avaliar as relações complexas entre a bacia de captação e o reservatório, é de grande importância na produção de diagnósticos consistentes. O modelo de tomada de decisão foi produzido por meio de literatura especializada, conhecimento de especialistas, Processo Hierárquico Analítico (AHP) e Avaliação Multicritério (MCE). O modelo foi desenvolvido para atuar em escala da paisagem, considerando a bacia de captação como a escala observacional. Foram selecionados sete critérios (variáveis preditoras) para determinar o grau de fragilidade ambiental para o processo de eutrofização antrópica em reservatórios tropicais: Variáveis Intrínsecas do Reservatório: (1) morfometria (profundidade) e Tempo de Retenção (RT); (2) Variáveis Limnológicas: Zona Eufótica e presença de macrófitas de crescimento intensivo; (3) Variáveis Antrópicas: Fonte de Poluição Difusa Potencial; e (4) Variáveis Hidrológicas: Taxa de Sedimentação e escoamento superficial potencial. No processo de ponderação das variáveis, todas as matrizes foram consistentes e os especialistas priorizaram o Escoamento Superficial potencial e a Fonte de Poluição Difusa Potencial com os principais responsáveis pelo processo de eutrofização antrópica. Apesar dos critérios e pesos serem fixos para os reservatórios tropicais, existe a possibilidade de ajuste do modelo de acordo com situações especiais, já que o modelo é flexível suficiente para ser utilizados em outras bacias de captação, com características diferentes e intrínsecas. O modelo também é adaptável em função da disponibilidade de base de dados / The human and natural systems are integrated systems, with complex interactions and a strong multidisciplinary character. The application of landscape limnology, as a holistic principle to address the complex relationships between the watershed and reservoir, are of great importance to produce consistent diagnoses and predictions. The GIS-based model was performed by using literature, expert knowledge, Analytical Hierarchy Process (AHP) and Multi-Criteria Evaluation (MCE). The model was performed in a landscape scale considering the watershed as the observational scale. We selected seven criteria (predictor variables) to assess the environmental fragility to human-induced eutrophication in tropical reservoirs: (1) Intrinsic Reservoir Variables: morphometry (depth) and Retention Time (RT); (2) Limnological Variables: euphotic zone and intensive-growth macrophytes presence; (3) Anthropic Variable: Potential Non-Point Source (NPS); and (4) Hydrological Variable: Sedimentation Rate and Potential Runoff. In the weighting process, all matrices were consistent and the experts prioritized the potential runoff and potential NPS criteria as the main drivers of human-induced eutrophication. Although the criteria and its weights are considered fixed for any tropical reservoir, it is possible to adjust them according to specific situations since the model proposed is flexible enough to be used in different watersheds with different and intrinsic characteristics and adapted according to the database availability

A evolução molecular, impulsionada pela Teoria Sintética da Evolução, tornou um assunto indispensável na compreensão da evolução da vida. O crescimento genômico, etapa responsável pelo maior potencial de armazenamento de informação e estabilidade, também foi submetido à indelével ação da seleção natural. Utilizando a metodologia dos ciclos de amplificação-mutação-seleção, tal como o SELEX (systematic evolution of ligands by exponential enrichment), que mimetizam a seleção natural, e ferramentas da Teoria de Informação, foram desenvolvidos e implementados programas para simular a evolução, considerando, além de outros, um parâmetro pouco explorado na literatura: a variação do tamanho do genoma. Foram estudados dois cenários distintos; no primeiro a seleção era dependente da busca exata de uma sequência pré-determinada (o filtro). Além disso, a entropia de Shannon considerada era referente ao alinhamento da molécula toda. Avaliando configurações simples desse modelo, foi possível desenvolver uma equação analítica que descreveu bem os resultados (para tamanho de genoma constante). No segundo cenário, foram exploradas a seleção não específica de uma sequência, o número máximo de bases constante, a entropia apenas das regiões de interesse e a presença de até cinco filtros de seleção. A entropia da molécula toda se mostrou pouco significativa (primeiro cenário), diferentemente da avaliada em apenas uma região. Foi possível observar que o crescimento do genoma foi pouco acentuado, predominando as moléculas menores, mesmo com grande quantidade de filtros, o que indica que o sistema está sob \"seleção por compressão\", sendo, pois, necessário atribuir explicitamente vantagens às moléculas mais complexas para poder haver aumento no crescimento médio. / Molecular Evolution, stimulated and supported by Evolution Synthetic Theory, became essential to understand evolution of life. Genomic growth was responsible to increase the capacity of storage information and to stability of the molecule; besides, it was also submitted to natural selection. Using amplification-mutation-selection methodology, such as SELEX (systematic evolution of ligands by exponential enrichment), and tools of Information Theory, it was developed computer program to simulate macromolecule evolution taking into account, besides other, a less study parameter in specialized journal: the genome size variation. It was studied two different scenarios. In the first one, selection was dependent of a specific sequence to be searched; moreover, Shannon entropy took into account all nucleotides of all molecules and it was studied with until two sequence target. It was possible develop an analytical equation to well describe simple settings of this model. In the second scenario, in another hand, selection depends on a specific sequence, but is not required to match the whole sequence. Also, to compute Shannon entropy it was taking into account only the least Hamming distance sequence of each molecule. It was studied until five sequence target in this scenario. Entropy was not significant in first scenario as it was in the second one. Size genome evolution shows the system were under compression selection, being necessary to get other advantage to become possible an increase in genome size.

Considere $N$ pontos distribuídos de forma aleatória e uniforme num hipercubo $d$-dimensional. Cada ponto representa um sítio num meio desordenado. Um caminhante explora este meio saltando para os sítios mais próximos, que não tenham sido visitados nos últimos $\\mu$ (memoria) passos, inclusive o próprio sítio. A trajetória do caminhante é composta de uma parte transiente e de uma parte periódica (ciclos). Neste caso, o viajante pode ou não explorar todos espaço disponível. A partir de uma memória crítica, ocorre uma transição entre os regimes de exploração localizado e estendido. Para sistemas unidimensionais, essa transição ocorre na memória crítica $\\mu_1=\\log_2{N}$. A regra determinista pode ser suavizada, a fim de considerar situações mais realistas, com a inclusão do parâmetro estocástico $T$ (temperatura). Agora, os movimentos do caminhante são definidos por uma função densidade de probabilidade (PDF) que é parametrizada por $T$ e por uma função custo, que cresce à medida que a distância entre os sítios cresce. A PDF é escolhida de forma a favorecer saltos para sítios mais próximos. Com o aumento da temperatura, o caminhante pode sair de ciclos e estender sua exploração. Aqui, nós apresentamos os estudos analíticos e numéricos sobre a influência da temperatura e da memória crítica na exploração de um meio desordenado unidimensional. / Consider $N$ sites randomly and uniformly distributed in a $d$-dimensional hypercube. A walker explores this disordered medium going to the nearest site, which has not been visited in the last $\\mu$ (memory) steps. The walker trajectory is composed of a transient part and a periodic part (cycles). In this case, travelers can or cannot explore all available space, given rise to a crossover at critical memory, for one-dimensional systems $\\mu_1=\\log_2{N}$, between localized and extended regimes. % as function of $\\mu$. The deterministic rule can be softened to consider more realistic situations with the inclusion of a stochastic parameter $T$ (temperature). In this case, the walker movement is defined by a probability density function (PDF) that is parameterized by $T$ and a cost function, which increases as the distance among sites increases. The PDF is chosen to favor hops to nearest sites. As the temperature increases, the walker can escape from cycles and extend the exploration. Here we report the analytical and numerical studies of the influence of the temperature and the critical memory in the exploration of a one-dimensional disordered system.