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Corrigendum to “Challenges, tasks, and opportunities in modeling agent-based complex systems”, Ecological Modeling, 2021, 457: 109,685, page 1–15

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Ecological Modelling 2022年 471卷

作者： Li An Volker Grimm Abigail Sullivan B.L. Turner Nicolas Malleson Alison Heppenstall Christian Vincenot Derek Robinson Xinyue Ye Jianguo Liu Emilie Lindkvist Wenwu Tang Center for Complex Human-Environment Systems San Diego State University 5500 Campanile Dr. San Diego CA 92182-4493 United States Dept of Geography San Diego State University 5500 Campanile Dr. San Diego CA 92182-4493 United States Helmholtz Centre for Environmental Research – UFZ Leipzig-Halle Department of Ecological Modelling Permoserstr. 15 04318 Leipzig Germany Environmental Resilience Institute Indiana University 717 E 8th Street Bloomington IN 47408 United States School of Geographical Sciences and Urban Planning & School of Sustainability Arizona State University PO Box 875302 Tempe AZ 85287-5302 United States Alan Turing Institute British Library 96 Euston Road London NW1 2DB United Kingdom Alison Heppenstall Centre for Spatial Analysis and Policy School of Geography University of Leeds Woodhouse Lane Leeds LS2 9JT United Kingdom Department of Social Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto 606-8501 Japan Department of Geography and Environmental Management University of Waterloo 200 University Avenue West Waterloo ON N2L 3G1 Canada Department of Landscape Architecture and Urban Planning & Urban Data Science Lab Texas A&M University College Station TX 77843 United States Center for Systems Integration and Sustainability Michigan State University East Lansing MI 48823-5243 United States Stockholm Resilience Centre Stockholm University Kräftriket 2B 106 91 Stockholm Sweden Department of Geography and Earth Sciences University of North Carolina at Charlotte 9201 University City Blvd Charlotte NC 28223 United States

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Decoupling Approximation Robustly Reconstructs Directed Dynamical Networks

arXiv

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arXiv 2017年

作者： Simidjievski, Nikola Tanevski, Jovan Ženko, Bernard Levnajić, Zoran Todorovski, Ljupčo Džeroski, Sašo Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Complex systems and Data science Lab Faculty of information studies in Novo mesto Novo mesto Slovenia Faculty of administration University of Ljubljana Ljubljana Slovenia

Methods for reconstructing the topology of complex networks from time-resolved observations of node dynamics are gaining relevance across scientific disciplines. Of biggest practical interest are methods that make no assumptions about the properties of the dynamics, and can cope with noisy, short and incomplete trajectories. Ideal reconstruction in such scenario requires an exhaustive approach of simulating the dynamics for all possible network configurations and matching the simulated against the actual trajectories, which of course is computationally too costly for any realistic application. Relying on insights from equation discovery and machine learning, we here introduce decoupling approximation of dynamical networks and propose a new reconstruction method based on it. Decoupling approximation consists of matching the simulated against the actual trajectories for each node individually rather than for the entire network at once. Despite drastic reduction of the computational cost that this approximation entails, we find our method’s performance to be very close to that of the ideal method. In particular, we not only make no assumptions about the properties of the trajectories, but provide strong evidence that our methods’ performance is largely independent of the dynamical regime at hand. Of crucial relevance for practical applications, we also find our method to be extremely robust to both length and resolution of the trajectories and relatively insensitive to noise. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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A common trajectory recapitulated by urban economies

arXiv

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arXiv 2018年

作者： Hong, Inho Frank, Morgan R. Rahwan, Iyad Jung, Woo-Sung Youn, Hyejin Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Industrial and Management Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Asia Pacific Center for Theoretical Physics Pohang37673 Korea Republic of Kellogg School of Management Northwestern University EvanstonIL60208 United States Northwestern Institute on Complex Systems EvanstonIL60208 United States London Mathematical Lab LondonWC2N 6DF United Kingdom Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Is there a general economic pathway recapitulated by individual cities over and over? Identifying such evolution structure, if any, would inform models for the assessment, maintenance, and forecasting of urban sustainability and economic success as a quantitative baseline. This premise seems to contradict the existing body of empirical evidences for path-dependent growth shaping the unique history of individual cities. And yet, recent empirical evidences and theoretical models have amounted to the universal patterns, mostly size-dependent, thereby expressing many of urban quantities as a set of simple scaling laws. Here, we provide a mathematical framework to integrate repeated cross-sectional data, each of which freezes in time dimension, into a frame of reference for longitudinal evolution of individual cities in time. Using data of over 100 millions employment in thousand business categories between 1998 and 2013, we decompose each city’s evolution into a pre-factor and relative changes to eliminate national and global effects. In this way, we show the longitudinal dynamics of individual cities recapitulate the observed cross-sectional regularity. Larger cities are not only scaled-up versions of their smaller peers but also of their past. In addition, our model shows that both specialization and diversification are attributed to the distribution of industry’s scaling exponents, resulting a critical population of 1.2 million at which a city makes an industrial transition into innovative economies. Copyright © 2018, The Authors. All rights reserved.

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IDTxl: The information dynamics toolkit xl: A python package for the efficient analysis of multivariate information dynamics in networks

arXiv

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arXiv 2018年

作者： Wollstadt, Patricia Lizier, Joseph T. Vicente, Raul Finn, Conor Martinez-Zarzuela, Mario Mediano, Pedro Novelli, Leonardo Wibral, Michael MEG Unit Brain Imaging Center Goethe-University Frankfurt Fankfurt am Main Germany Centre for Complex Systems Faculty of Engineering and IT University of Sydney SydneyNSW Australia Computational Neuroscience Lab Institute of Computer Science Tartu Estonia Data61 CSIRO EppingNSW Australia Communications and Signal Theory and Telematics Engineering University of Valladolid Valladolid Spain Computational Neurodynamics Group Department of Computing Imperial College London London United Kingdom Max Planck Institute for Dynamics and Self-Organization Göttingen Germany Campus Institute for Dynamics of Biological Networks Georg-August Universität Göttingen Germany

We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://***/pwollstadt/IDTxl). Information theory (Cover & Thomas, 2006;MacKay, 2003;Shannon, 1948) is the mathematical theory of information and its transmission over communication channels. Information theory provides quantitative measures of the information content of a single random variable (entropy) and of the information shared between two variables (mutual information). The defined measures build on probability theory and solely depend on the probability distributions of the variables involved. As a consequence, the dependence between two variables can be quantified as the information shared between them, without the need to explicitly model a specific type of dependence. Hence, mutual information is a model-free measure of dependence, which makes it a popular choice for the analysis of systems other than communication channels. Transfer entropy (TE) (Schreiber, 2000) is an extension of mutual information that measures the directed information transfer between time series of a source and a target variable. TE has become popular in many scientific disciplines to infer dependencies and whole networks from data. Notable application domains include neuroscience (Wibral, Vicente, & Lindner, 2014) and dynamical systems analysis (Lizier, Prokopenko, & Zomaya, 2014) (see Bossomaier, Barnett, Harré, & Lizier (2016) for an introduction to TE and a comprehensive discussion of its application). In the majority of the applications, TE is used in a bivariate fashion, where information transfer is quantified between all sourcetarget pairs. In a multivariate setting, however, such a bivariate analysis may infer spurious or redundant interactions, where multiple sources provide the same information about the target. Conversely, bivariate analysis may also miss synergistic

关键词： High level languages

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Stroke genetics informs drug discovery and risk prediction across ancestries (vol 611, pg 115, 2022)

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NATURE 2022年第7938期612卷 E7-E7页

作者： Mishra, Aniket Malik, Rainer Hachiya, Tsuyoshi Jurgenson, Tuuli Namba, Shinichi Posner, Daniel C. Kamanu, Frederick K. Koido, Masaru Le Grand, Quentin Shi, Mingyang He, Yunye Georgakis, Marios K. Caro, Ilana Krebs, Kristi Liaw, Yi-Ching Vaura, Felix C. Lin, Kuang Winsvold, Bendik Slagsvold Srinivasasainagendra, Vinodh Parodi, Livia Bae, Hee-Joon Chauhan, Ganesh Chong, Michael R. Tomppo, Liisa Akinyemi, Rufus Roshchupkin, Gennady V. Habib, Naomi Jee, Yon Ho Thomassen, Jesper Qvist Abedi, Vida Carcel-Marquez, Jara Nygaard, Marianne Leonard, Hampton L. Yang, Chaojie Yonova-Doing, Ekaterina Knol, Maria J. Lewis, Adam J. Judy, Renae L. Ago, Tetsuro Amouyel, Philippe Armstrong, Nicole D. Bakker, Mark K. Bartz, Traci M. Bennett, David A. Bis, Joshua C. Bordes, Constance Borte, Sigrid Cain, Anael Ridker, Paul M. Cho, Kelly Chen, Zhengming Cruchaga, Carlos Cole, John W. de Jager, Phil L. de Cid, Rafael Endres, Matthias Ferreira, Leslie E. Geerlings, Mirjam I. Gasca, Natalie C. Gudnason, Vilmundur Hata, Jun He, Jing Heath, Alicia K. Ho, Yuk-Lam Havulinna, Aki S. Hopewell, Jemma C. Hyacinth, I. Hyacinth Inouye, Michael Jacob, Mina A. Jeon, Christina E. Jern, Christina Kamouchi, Masahiro Keene, Keith L. Kitazono, Takanari Kittner, Steven J. Konuma, Takahiro Kumar, Amit Lacaze, Paul Launer, Lenore J. Lee, Keon-Joo Lepik, Kaido Li, Jiang Li, Liming Manichaikul, Ani Markus, Hugh S. Marston, Nicholas A. Meitinger, Thomas Mitchell, Braxton D. Montellano, Felipe A. Morisaki, Takayuki Mosley, Thomas H. Nalls, Mike A. Nordestgaard, Borge G. O'Donnell, Martin J. Okada, Yukinori Onland-Moret, N. Charlotte Ovbiagele, Bruce Peters, Annette Psaty, Bruce M. Rich, Stephen S. Rosand, Jonathan Sabatine, Marc S. Sacco, Ralph L. Saleheen, Danish Sandset, Else Charlotte Salomaa, Veikko Sargurupremraj, Muralidharan Sasaki, Makoto Satizabal, Claudia L. Schmidt, Carsten O. Shimizu, Atsushi Smith, Nicholas L. Sloane, Kelly L. Sutoh, Yoichi Sun, Yan V. Tanno, Kozo Tiedt, Steffen Tatlisumak, Turgut Torres-Aguila, Nuria P. Tiwari, Hemant K. Tregouet, Bordeaux Population Health Research Center University of Bordeaux Inserm UMR 1219 Bordeaux France Department of Public Health Bordeaux University Hospital Bordeaux France Department of Neurology Memory Clinic Bordeaux University Hospital Bordeaux France Department of Neurology Institute for Neurodegenerative Diseases CHU de Bordeaux Bordeaux France Institute for Stroke and Dementia Research (ISD) University Hospital LMU Munich Munich Germany Center for Genomic Medicine Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of Harvard and the Massachusetts Institute of Technology Cambridge MA USA Munich Cluster for Systems Neurology Munich Germany German Center for Neurodegenerative Diseases (DZNE) Munich Germany Iwate Tohoku Medical Megabank Organization Iwate Medical University Iwate Japan Estonian Genome Centre Institute of Genomics University of Tartu Tartu Estonia Institute of Mathematics and Statistics University of Tartu Tartu Estonia Department of Computational Biology University of Lausanne Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland University Center for Primary Care and Public Health Lausanne Switzerland Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan Department of Genome Informatics Graduate School of Medicine The University of Tokyo Tokyo Japan Laboratory for Systems Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Laboratory of Statistical Immunology Immunology Frontier Research Center (WPI-IFReC) Osaka University Suita Japan Integrated Frontier Research for Medical Science Division Institute for Open and Transdisciplinary Research Initiatives Osaka University Suita Japan Center for Infectious Disease Education and Research (CiDER) Osaka University Suita Japan Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA TIMI S

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Author Correction: Common variants in Alzheimer's disease and risk stratification by polygenic risk scores

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Nature communications 2023年第1期14卷 716页

作者： Itziar de Rojas Sonia Moreno-Grau Niccolo Tesi Benjamin Grenier-Boley Victor Andrade Iris E Jansen Nancy L Pedersen Najada Stringa Anna Zettergren Isabel Hernández Laura Montrreal Carmen Antúnez Anna Antonell Rick M Tankard Joshua C Bis Rebecca Sims Céline Bellenguez Inés Quintela Antonio González-Perez Miguel Calero Emilio Franco-Macías Juan Macías Rafael Blesa Laura Cervera-Carles Manuel Menéndez-González Ana Frank-García Jose Luís Royo Fermin Moreno Raquel Huerto Vilas Miquel Baquero Mónica Diez-Fairen Carmen Lage Sebastián García-Madrona Pablo García-González Emilio Alarcón-Martín Sergi Valero Oscar Sotolongo-Grau Abbe Ullgren Adam C Naj Afina W Lemstra Alba Benaque Alba Pérez-Cordón Alberto Benussi Alberto Rábano Alessandro Padovani Alessio Squassina Alexandre de Mendonça Alfonso Arias Pastor Almar A L Kok Alun Meggy Ana Belén Pastor Ana Espinosa Anaïs Corma-Gómez Angel Martín Montes Ángela Sanabria Anita L DeStefano Anja Schneider Annakaisa Haapasalo Anne Kinhult Ståhlbom Anne Tybjærg-Hansen Annette M Hartmann Annika Spottke Arturo Corbatón-Anchuelo Arvid Rongve Barbara Borroni Beatrice Arosio Benedetta Nacmias Børge G Nordestgaard Brian W Kunkle Camille Charbonnier Carla Abdelnour Carlo Masullo Carmen Martínez Rodríguez Carmen Muñoz-Fernandez Carole Dufouil Caroline Graff Catarina B Ferreira Caterina Chillotti Chandra A Reynolds Chiara Fenoglio Christine Van Broeckhoven Christopher Clark Claudia Pisanu Claudia L Satizabal Clive Holmes Dolores Buiza-Rueda Dag Aarsland Dan Rujescu Daniel Alcolea Daniela Galimberti David Wallon Davide Seripa Edna Grünblatt Efthimios Dardiotis Emrah Düzel Elio Scarpini Elisa Conti Elisa Rubino Ellen Gelpi Eloy Rodriguez-Rodriguez Emmanuelle Duron Eric Boerwinkle Evelyn Ferri Fabrizio Tagliavini Fahri Küçükali Florence Pasquier Florentino Sanchez-Garcia Francesca Mangialasche Frank Jessen Gaël Nicolas Geir Selbæk Gemma Ortega Geneviève Chêne Georgios Hadjigeorgiou Giacomina Rossi Gianfranco Spalletta Giorgio Giaccone Giulia Grande Giuliano Binetti Goran Papenberg Harald Hampel Research Center and Memory clinic Fundació ACE Institut Català de Neurociències Aplicades Universitat Internacional de Catalunya Barcelona Spain. CIBERNED Network Center for Biomedical Research in Neurodegenerative Diseases National Institute of Health Carlos III Madrid Spain. Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Section Genomics of Neurodegenerative Diseases and Aging Department of Clinical Genetics Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Delft Bioinformatics Lab Delft Univeristy of Technology Delft The Netherlands. Univ. Lille Inserm Institut Pasteur de Lille CHU Lille U1167-Labex DISTALZ-RID-AGE-Risk Factors and Molecular Determinants of Aging-Related Diseases Lille France. Division of Neurogenetics and Molecular Psychiatry Department of Psychiatry and Psychotherapy University of Cologne Medical Faculty Cologne Germany. Department of Neurodegenerative diseases and Geriatric Psychiatry University Clinic Bonn Bonn Germany. Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research Amsterdam Neuroscience VU University Amsterdam The Netherlands. Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden. Amsterdam UMC-Vrije Universiteit Amsterdam Department of Epidemiology and Data Science Amsterdam Public Health Research Institute Amsterdam The Netherlands. Neuropsychiatric Epidemiology Unit Department of Psychiatry and Neurochemistry Institute of Neuroscience and Physiology Sahlgrenska Academy Centre for Ageing and Health (AgeCap) University of Gothenburg Gothenburg Sweden. Unidad de Demencias Hospital Clínico Universitario Virgen de la Arrixaca Murcia Spain. Alzheimer's disease and other cognitive disorders unit. Service of Neurology Hospital Clínic of Barcelona. Institut d'Investigacions Biomèdiques August Pi i Sunyer University of Barcelona Barcelona Spain

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Pathwise uniqueness of multi-dimensional stochastic differential equations with H(o)lder diffusion coefficients

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中国数学前沿 2011年第1期6卷 129-136页

作者： Dejun LUO Key Lab of Random Complex Structures and Data Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

We extend Yamada-Watababe's criterion [J. Math. Kyoto Univ.,1971, 11: 553-563] on the pathwise uniqueness of one-dimensional stochastic differential equations to a special class of multi-dimensional stochastic dif... 详细信息

关键词： Stochastic differential equation (SDE) strong solution pathwise uniqueness H(o)lder continuity

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Reports of the AAAI 2010 conference workshops

Reports of the AAAI 2010 conference workshops

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作者： Aha, David W. Boddy, Mark Bulitko, Vadim D'Avila Garcez, Artur S. Doshi, Prashant Edelkamp, Stefan Geib, Christopher Gmytrasiewicz, Piotr Goldman, Robert P. Halevy, Alon Hitzler, Pascal Isbell, Charles Josyula, Darsana Kaelbling, Leslie Pack Kersting, Kristian Kunda, Maithilee Lamb, Luis C. Marthi, Bhaskara McGreggor, Keith Mihalkova, Lilyana Nastase, Vivi Natarajan, Sriraam Provan, Gregory Raja, Anita Ram, Ashwin Riedl, Mark Russell, Stuart Sabharwal, Ashish Smaus, Jan-Georg Sukthankar, Gita Tuyls, Karl Van Der Meyden, Ron Adaptive Systems Section Naval Research Laboratory Washington DC United States Adventium Labs. Minneapolis MN United States Department of Computer Science University of Alberta Canada City University London United Kingdom University of Georgia United States University of Bremen Germany School of Informatics University of Edinburgh United Kingdom University of Illinois Chicago United States Smart Information Flow Technologies LLC Minneapolis MN United States Structured Data Group Google Research United States Department of Computer Science Wright State University Dayton OH United States School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science Bowie State University United States Department of Computer Science and Engineering Massachusetts Institute of Technology United States Statistical Relational Mining Group Fraunhofer IAIS Germany Department of Computer Science Federal University of Rio Grande Do sul Brazil Willow Garage Inc. CA United States Computer Science Department University of Maryland College Park United States Heidelberg Institute for Theoretical Studies Heidelberg Germany Department of Computer Science University of Wisconsin Madison United States University College Cork Ireland Cork Complex Systems Lab. Ireland Department of Software and Information Systems University of North Carolina Charlotte United States Department of Computer Science and Engineering University of California Berkeley United States Department of Computer Science Cornell University United States Institute for Computational Sustainability United States University of Freiburg Germany School of Electrical Engineering and Computer Science University of Central Florida United States Maastricht University Netherlands University of New South Wales NICTA Australia

The AAAI-10 workshop program was held on July 11-12, 2010, at the Westin Peachtree Plaza in Atlanta, Georgia. The workshop program included 13 workshops covering a wide range of topics in artificial intelligence. There were several presentations on particular formalisms for combining the levels of planning from the perspectives of classical planning, motion planning, POMDPs, search-based planning, and temporal logic-based constraints. Henry Lieberman from Massachusetts Institute of Technology Media laboratory presented work developing a platform for knowledge acquisition and usage from expert users, built upon experience with eliciting information from nonexperts in a collaborative manner. The objective of the AAAI Workshop on Goal-Directed Autonomy (GDA) was to encourage discussion and novel contributions on intelligent agents that can self-select their goals. Jeremy Baxter from QinetiQ focused on user interaction, while Nick Hawes described a goal-directed reasoning framework for controlling a mobile robot.

关键词： Motion planning

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Extraterrestrial Subsurface Technology Test Bed: Human Use and Scientific Value of Martian Caves

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AIP Conference Proceedings 2004年第1期699卷

作者： P. J. Boston R. D. Frederick S. M. Welch J. Werker T. R. Meyer B. Sprungman V. Hildreth‐Werker S. L. Thompson Earth & Environmental Sciences Dept. New Mexico Institute of Mining and Technology 801 Leroy Place Socorro NM 87801 Complex Systems Research Inc. Boulder CO 80306 Oregon Public Education Network Clearinghouse Silverton OR Boulder Center for Science and Policy Boulder CO Space Data Resources and Information Boulder CO Southwest Composites Hillsboro NM Lawrence Livermore Nat. Lab. Livermore CA

Caves and subsurface voids on Mars and other bodies can provide significant advantages when used for human habitat and operational space. They are also targets for significant scientific investigations. We have developed a suite of technology concepts to make utilization of extraterrestrial caves feasible. Our goal is to provide a solid foundation of information and options so that mission planners may realistically consider the subsurface option in development of mission scenarios. We have concentrated on identifying the primary challenges to human operation in the extraterrestrial cave environment. Employing concepts like inflatable cave liner habitats, foamed‐in‐place airlock technologies, and micro‐robotic self‐deploying communication, mapping, and data transmission networks, we are developing solutions to meet those challenges and demonstrate the feasibility of cave use on Mars. We present the results of field trials of an incave mission simulation in several Earth caves as a proof‐of‐concept demonstration. These trials during the summer and fall of 2003 culminate in a high fidelity simulation scheduled for January 2004. Concepts and prototypes were developed during a completed Phase I and on‐going Phase II NASA Institute for Advanced Concepts (NIAC) study.

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