Hasil untuk "Human ecology. Anthropogeography"

Haichang Li, Anjun Zhu, Arpit Narechania

In real-world collaboration, alignment, process structure, and outcome quality do not exhibit a simple linear or one-to-one correspondence: similar alignment may accompany either rapid convergence or extensive multi-branch exploration, and lead to different results. Existing accounts often isolate these dimensions or focus on specific participant types, limiting structural accounts of collaboration. We reconceptualize collaboration through two complementary lenses. The task lens models collaboration as trajectory evolution in a structured task space, revealing patterns such as advancement, branching, and backtracking. The intent lens examines how individual intents are expressed within shared contexts and enter situated decisions. Together, these lenses clarify the structural relationships among alignment, decision-making, and trajectory structure. Rather than reducing collaboration to outcome quality or treating alignment as the sole objective, we propose a unified dynamic view of the relationships among alignment, process, and outcome, and use it to re-examine collaboration structure across Human-Human, AI-AI, and Human-AI settings.

Nikita Soni, Dhruv Vijay Kunjadiya, Pratham Piyush Shah et al.

Language model training and inference ignore a fundamental linguistic fact -- there is a dependence between multiple sequences of text written by the same person. Prior work has shown that addressing this form of \textit{ecological fallacy} can greatly improve the performance of multiple smaller (~124M) GPT-based models. In this work, we ask if addressing the ecological fallacy by modeling the author's language context with a specific LM task (called HuLM) can provide similar benefits for a larger-scale model, an 8B Llama model. To this end, we explore variants that process an author's language in the context of their other temporally ordered texts. We study the effect of pre-training with this author context using the HuLM objective, as well as using it during fine-tuning with author context (\textit{HuFT:Human-aware Fine-Tuning}). Empirical comparisons show that addressing the ecological fallacy during fine-tuning alone using QLoRA improves the performance of the larger 8B model over standard fine-tuning. Additionally, QLoRA-based continued HuLM pre-training results in a human-aware model generalizable for improved performance over eight downstream tasks with linear task classifier training alone. These results indicate the utility and importance of modeling language in the context of its original generators, the authors.

Aaron An

Abstract Urban planning is challenging especially rapidly developing areas, making it imperative for decision-makers to be transparent, fair, and data-driven. This study looks at how planning decisions are made in local government in Australia. It compares two cases in Wyndham City where communities asked for the creation of new suburbs. The research uses evidence from council records and voting outcomes. It shows how one vote changed the final decision in both cases. In one case, a vote went against what the community wanted. This finding shows how personal bias and poor judgment can appear in traditional systems of governance. The study finds that technology-assisted decision-making (TADM) can improve openness, fairness, and accountability. The use of new technologies can make decision-making more consistent and more based on evidence. It can also help reflect public views more accurately. The study concludes that TADM should become part of urban governance. It recommends more research and real-world testing to build systems that support fair and sustainable planning outcomes.

Martijn IJtsma, Salvatore Hargis

Studies of human-robot interaction in dynamic and unstructured environments show that as more advanced robotic capabilities are deployed, the need for cooperative competencies to support collaboration with human problem-holders increases. Designing human-robot systems to meet these demands requires an explicit understanding of the work functions and constraints that shape the feasibility of alternative joint work strategies. Yet existing human-robot interaction frameworks either emphasize computational support for real-time execution or rely on static representations for design, offering limited support for reasoning about coordination dynamics during early-stage conceptual design. To address this gap, this article presents a novel computational framework for analyzing joint work strategies in human-robot systems by integrating techniques from functional modeling with graph-theoretic representations. The framework characterizes collective work in terms of the relationships among system functions and the physical and informational structure of the work environment, while explicitly capturing how coordination demands evolve over time. Its use during conceptual design is demonstrated through a case study in disaster robotics, which shows how the framework can be used to support early trade-space exploration of human-robot coordination strategies and to identify cooperative competencies that support flexible management of coordination overhead. These results show how the framework makes coordination demands and their temporal evolution explicit, supporting design-time reasoning about cooperative competency requirements and work demands prior to implementation.

Emmanuel Fashae, Michael Burke, Leimin Tian et al.

Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning.

Jessica He, Stephanie Houde, Justin D. Weisz

AI systems powered by large language models can act as capable assistants for writing and editing. In these tasks, the AI system acts as a co-creative partner, making novel contributions to an artifact-under-creation alongside its human partner(s). One question that arises in these scenarios is the extent to which AI should be credited for its contributions. We examined knowledge workers' views of attribution through a survey study (N=155) and found that they assigned different levels of credit across different contribution types, amounts, and initiative. Compared to a human partner, we observed a consistent pattern in which AI was assigned less credit for equivalent contributions. Participants felt that disclosing AI involvement was important and used a variety of criteria to make attribution judgments, including the quality of contributions, personal values, and technology considerations. Our results motivate and inform new approaches for crediting AI contributions to co-created work.

Maan Barua

This article develops a wider ontology of infrastructure. It argues that infrastructures not only hasten the flow of materials but produce non-human mobilities and immobilities that radically alter the dynamics of life. Infrastructures become a medium of life as natural and infrastructural ecologies meld, reorienting notions of design, architecture, planning and governance. Non-human life itself can be cast as infrastructure, with biopolitical implications for anticipating and managing the future. An infrastructural ontology moving beyond anthropocentric familiars generates new analytics and critical openings for the politics of governing human and non-human life.

Abdolmajid Amirzadeh Ghasri, Saeideh Kalantari, Mahdi Tazeh et al.

Extended Abstract IntroductionToday, erosion is a major factor in land degradation in Iran. Since a large portion of Iran is located in arid and semi-arid climates, wind erosion can be an effective means of destroying and causing damage in these areas. Nabkhas have distinct morphometric components from other erosional forms, but some of these parameters can affect the amount of wind sediment transfer. Identifying and measuring the relationship between these parameters and examining the process of changes in certain physical and chemical characteristics of sediments can have a significant impact on planning operations to reduce wind sand transfer and improve the analysis of sediment characteristics. The phenomenon of sand entering human centers in the southeast of Kerman province and Rigan city is considered a problem. This research aims to investigate the impact of the morphology of the species Tamarix, Calligonum, and Iranian mesquite on the morphometrics of the nabkhas in Rigan city and analyzing the measured parameters of the Nabkhas morphometry and their correlations using statistical methods. Material and MethodsThe city of Rigan, which covers 8600 square kilometers, is located south of Kerman. This city is one of the most important centers with the highest priority. Aerial photographs and Google Earth images were used to determine the area of Nabkhas, and a field visit was conducted to assess the development of Nebkas' territory.This area is a major source of wind erosion in Kerman province and even in the country. The severity of erosion is such that date trees with a height of more than 5 meters are buried under sand sediments. The first step was to measure the morphometric characteristics of Nabkhas along 6 one-kilometer transects. The characteristics of the Nabkhas of each of the Calligonum, Iranian mesquite, and Tamarix plants were measured separately. To investigate the characteristics of Nabkhas, the morphological characteristics of Nabkhas including height and diameter of the base were measured. The characteristics of the vegetation that forms Nabkhas were studied by measuring plant morphological factors, such as crown diameter and plant height. Statistics were collected from 44 farms that use Tamarix plants, 51 farms that use Iranian mesquite plants, and 38 farms that use Calligonum plants in total.Results The regression analysis between plant height and Nabkhas height indicates that the slope of changes in Nabkhas height compared to changes in vegetation height is higher in sedge and mesquite plants than in Calligonum. The height of Nebka is more affected by Tamarix changes than the other two plants. Based on the evaluation of the regression results between the canopy diameter and the canopy height, it can be concluded that the slope of the canopy height changes is greater than the slope of the canopy diameter changes in Calligonum and Iranian mesquite plants, respectively. It can be concluded that the Calligonum plant's crown diameter has a greater effect on Nabkhas' height changes than the other two plants. The regression results between plant height and root diameter indicate that the slope of root diameter changes is greater than that of plant height changes, respectively. It can be concluded that the height changes of the Tamarix plant are more significant in influencing the diameter changes of the Nabkhas base than the other two plants. The slope of diameter changes of the base compared to the changes of diameter of the plant crown, respectively, is higher in calligonum and mesquite plants than in Tamarix. Changes in the diameter of the crown of the Calligonum plant have a greater effect than the changes in the diameter of the other two plants on the changes in the diameter of the base of Nabkhas. Results and DiscussionNabkhas with Iranian mesquite have a longer base length in proportion to height than Nebkhas with Tamarix and Calligonum. The geomorphological characteristics of four plant species in Lut Plain's western region were examined, and it was found that the nebkhas with Tamarix have an average height of 1.5 meters and an average base of 4.2 meters. The Nabkhas' Iranian mesquite has an average height of 1.5 meters and an average base of 1.6 meters. Thus, their research results are in accord with the results of this research. According to the correlation results, the morphology of Tamarix, Calligonum, and Iranian mesquite species is significant compared to the morphometric characteristics of Nabkhas, which is consistent with the results (2 and 16). According to the regression results, Tamarix plant has a higher slope of changes in height compared to vegetation. The Calligonum plant has a higher slope of changes in height compared to diameter of plant canopy compared to the other two plants. On the other hand, the slope of changes in the diameter of the Nabkhas base compared to the changes in plant height of Tamarix is greater than that of the other two plants. The slope of the changes in the diameter of the base compared to the changes in the diameter of the plant crown is greater than that of the other two plants. The morphological characteristics of the sediments in the studied area indicate that storms are extremely strong throughout the year, causing a significant movement of sand in the area.

Xu Han, Maria Koliou

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

Jiao Li, Xiaolin Qiao, Zhibao Cheng et al.

Abstract Replacing the viscous damper of tuned mass damper (TMD) with the proposed inerter-enhanced dampers (IEDs), novel vibration mitigation methods, namely the IED-TMDs, are proposed. Unlike the TMD, which brings only one additional freedom into the system, the proposed IED-TMDs introduce more freedoms into the considered dynamic system. As a result, the traditional fixed-point theory cannot be used. To address this issue, this paper develops an extended fixed-point theory. Firstly, the inerter and the springs of the IED-TMDs are optimized considering that all four fixed points are of the same height. The closed-form solutions for the optimal inerter and springs of the IED-TMDs are obtained. Secondly, to obtain the optimal damping ratio for the IED-TMDs with multi-fixed points, a new optimization criterion is introduced. Different from the traditional fixed-point theory which controls the slope of the transfer function at the fixed points, the new optimization criterion assumes that the local peaks of the transfer function in between the four fixed points have the same height as the fixed points. And, a flat plateau is achieved in the transfer function. Further, the closed-form solutions for the optimal damping ratio are simplified in consideration of actual applications. Finally, the vibration mitigation performance of the IED-TMDs is evaluated. Results show that the vibration mitigation performance of IED-TMDs is superior to that of the conventional TMD. This superior vibration mitigation performance is more significant for the IED-TMDs with a smaller mass ratio.

Yi-Shiuan Tung, Matthew B. Luebbers, Alessandro Roncone et al.

Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those models' utility for interactions requiring coordination, including safety-critical or close-proximity tasks. Our key insight is that robot teammates can deliberately configure shared workspaces prior to interaction in order to reduce the variance in human motion, realizing classifier-agnostic improvements in goal prediction. In this work, we present an algorithmic approach for a robot to arrange physical objects and project "virtual obstacles" using augmented reality in shared human-robot workspaces, optimizing for human legibility over a given set of tasks. We compare our approach against other workspace arrangement strategies using two human-subjects studies, one in a virtual 2D navigation domain and the other in a live tabletop manipulation domain involving a robotic manipulator arm. We evaluate the accuracy of human motion prediction models learned from each condition, demonstrating that our workspace optimization technique with virtual obstacles leads to higher robot prediction accuracy using less training data.

Hyo Jin Do, Rachel Ostrand, Justin D. Weisz et al.

While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advancements have been made in algorithms that detect hallucinated content by assessing the factuality of the model's responses and attributing sections of those responses to specific source documents. However, there is limited research on how to effectively communicate this information to users in ways that will help them appropriately calibrate their trust toward LLMs. To address this issue, we conducted a scenario-based study (N=104) to systematically compare the impact of various design strategies for communicating factuality and source attribution on participants' ratings of trust, preferences, and ease in validating response accuracy. Our findings reveal that participants preferred a design in which phrases within a response were color-coded based on the computed factuality scores. Additionally, participants increased their trust ratings when relevant sections of the source material were highlighted or responses were annotated with reference numbers corresponding to those sources, compared to when they received no annotation in the source material. Our study offers practical design guidelines to facilitate human-LLM collaboration and it promotes a new human role to carefully evaluate and take responsibility for their use of LLM outputs.

Juli G. Pausas, Alexandro B. Leverkus

Abstract We define societal disturbances as discrete events that abruptly disrupt the functioning of human societies. There is a variety of such events, including hurricanes, floods, epidemics, nuclear accidents, earthquakes and wars, among others. These disturbances can interact, further increasing their impacts. The severity of disturbances does not only depend on their intrinsic properties (type, intensity and magnitude) but also greatly on human aspects (socioeconomic, historical, political and cultural aspects that define vulnerability). Very large or severe disturbances are infrequent and unpredictable. Yet societal disturbances are intrinsic to human societies; they have occurred through the entire human history and will continue to occur in the future. We can increase preparedness and recovery capacity but cannot avoid disturbances. The type, regime and scale of disturbances change with the development of societies. The increase in population density and complexity also increases the severity of many disturbances. Societal disturbances can temporarily disrupt the functioning of societies. However, when those disturbances are frequent, societies adapt to them and thus disturbances contribute to shape cultural evolution. That is, societal disturbances have a cost at short temporal scales, but they can build up resilience at mid‐ to long‐term scales. Understanding this dynamic view of human systems is becoming more important as climate is changing, humans are overexploiting natural resources and humanity is dense and hyperconnected. We need to take advantage of frequent small disturbances, as they can build resilience and reduce the likelihood of infrequent large and severe disturbances. Our challenge is to encourage actions and policies to be prepared for unknown, unpredictable and unprecedented (infrequent) large‐scale societal disturbances that will surely arrive. Read the free Plain Language Summary for this article on the Journal blog.

Ali Ayub, Jainish Mehta, Zachary De Francesco et al.

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github.com/aliayub7/cl_hri.

Yannick Metz, David Lindner, Raphaël Baur et al.

To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider human factors involved in providing feedback of different types. However, the systematic study of learning from diverse types of feedback is held back by limited standardized tooling available to researchers. To bridge this gap, we propose RLHF-Blender, a configurable, interactive interface for learning from human feedback. RLHF-Blender provides a modular experimentation framework and implementation that enables researchers to systematically investigate the properties and qualities of human feedback for reward learning. The system facilitates the exploration of various feedback types, including demonstrations, rankings, comparisons, and natural language instructions, as well as studies considering the impact of human factors on their effectiveness. We discuss a set of concrete research opportunities enabled by RLHF-Blender. More information is available at https://rlhfblender.info/.

Younes Lakhnati, Max Pascher, Jens Gerken

In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable autonomy through the means of verbal human-robot communication. In this paper, we introduce a novel framework for such a GPT-powered multi-robot testbed environment, based on a Unity Virtual Reality (VR) setting. This system allows users to interact with robot agents through natural language, each powered by individual GPT cores. By means of OpenAI's function calling, we bridge the gap between unstructured natural language input and structure robot actions. A user study with 12 participants explores the effectiveness of GPT-4 and, more importantly, user strategies when being given the opportunity to converse in natural language within a multi-robot environment. Our findings suggest that users may have preconceived expectations on how to converse with robots and seldom try to explore the actual language and cognitive capabilities of their robot collaborators. Still, those users who did explore where able to benefit from a much more natural flow of communication and human-like back-and-forth. We provide a set of lessons learned for future research and technical implementations of similar systems.

Claudia Canedoli, G. Ficetola, Davide Corengia et al.

Emily B. McCallen, Jonathan A. Knott, Gabriela C. Nunez‐Mir et al.

E has grown dramatically as a discipline over the past 100 years from a subdiscipline of biology into its own discipline with many subdisciplines (Kingsland 2005; Ayres 2012). The goal of ecology is to understand the relationships between organisms and their environment, and to use these relationships to help address various complex and challenging environmental problems (Figure 1). As with any expanding scientific field, ecology has evolved from a focus on a central set of core principles into multiple lines of subdisciplinespecific inquiry, resulting in a flourishing of ecological literature (WebFigure 1). Ecologists have periodically assessed the important concepts (Cherrett 1989; Reiners et al. 2017), questions (Sutherland et al. 2013), and themes (Thompson et al. 2001) in ecology to understand the trajectory of the field and to evaluate the societal relevance of the discipline. Comprehensive assessments provide an overview of the intellectual structure of the field, present a framework for pedagogical development and curriculum design, offer guidance for new research directions, and can sometimes help identify paradigm shifts in the science. Previous attempts to determine and define these key concepts, questions, and themes have relied on tools such as literature reviews, professional surveys, and synthesized information in textbooks. Surveys of professionals in the field offer insight into what the scientific community perceives as important (Reiners et al. 2017), but may not convey the topics that are actually being addressed in current research (perceived versus realized importance). Literature reviews, on the other hand, can provide useful syntheses of a body of research. However, it is impossible for a single researcher to keep up with the rapidly growing amount of available literature, making it increasingly difficult to see the overarching picture of ecology. Moreover, the selection of studies to be included in such reviews is prone to unintended human biases, even when selection procedures are established ahead of time (Murtaugh 2002). The development of automated content analysis (ACA) has given researchers the means to review vast amounts of literature in a more datadriven, unbiased manner. ACA is an innovative method for qualitative and quantitative text mining that uses text parsing and machine learning to identify the main concepts and themes discussed within a body of literature (NunezMir et al. 2016). ACA methods have been applied for reviews in forestry (NunezMir et al. 2015, 2017) and other fields (Chen and Bouvain 2009; Cretchley et al. 2010; Travaglia et al. 2011), but not to describe the overarching themes across the entire discipline of ecology. We contend that ACA can provide the systematic, datadriven analyses across time that are Trends in ecology: shifts in ecological research themes over the past four decades

Erica A. Newman

With the accumulating evidence of changing disturbance regimes becoming increasingly obvious, there is potential for disturbance ecology to become the most valuable lens through which climate-related disturbance events are interpreted. In this paper, I revisit some of the central themes of disturbance ecology and argue that the knowledge established in the field of disturbance ecology continues to be relevant to ecosystem management, even with rapid changes to disturbance regimes and changing disturbance types in local ecosystems. Disturbance ecology has been tremendously successful over the past several decades at elucidating the interactions between disturbances, biodiversity, and ecosystems, and this knowledge can be leveraged in different contexts. Primarily, management in changing and uncertain conditions should be focused primarily on the long-term persistence of that native biodiversity which has evolved within the local disturbance regime, and is likely to go extinct with rapid changes to disturbance intensity, frequency, and type. Where possible, conserving aspects of natural disturbance regimes will be vital to preserving functioning ecosystems and to that native biodiversity that requires disturbance for its continued existence, though these situations may become more limited over time. Finally, scientists must actively propose management policies that incorporates knowledge of disturbance ecology. Successful policies regarding changing disturbance regimes for biodiversity will not merely be reactive, and will recognize that for natural ecosystems as for human society, not all desired outcomes are simultaneously possible.

M. Sinnott‐Armstrong, Rocío Deanna, Chelsea Pretz et al.

Abstract Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the existence of some classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have afflicted research into syndromes in macroevolution and ecology. First, correlated evolution of traits and hypothesized selective drivers is often relied on as the only evidence for adaptation of those traits to those hypothesized drivers, without supporting evidence. Second, the selective driver is often inferred from a combination of traits without explicit testing. Third, researchers often measure traits that are easy for humans to observe rather than measuring traits that are suited to testing the hypothesis of adaptation. Finally, species are often chosen for study because of their striking phenotypes, which leads to the illusion of syndromes and divergence. We argue that these issues can be avoided by combining studies of trait variation across entire clades or communities with explicit tests of adaptive hypotheses and that taking this approach will lead to a better understanding of syndrome‐like evolution and its drivers.