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  • 1. Almberg, Wah-Sui
    et al.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    An active agent portfolio algorithm2003In: Artificial intelligence and computer science, Nova publishers , 2003, 1, p. 123-134Chapter in book (Refereed)
    Abstract [en]

    An algorithm for managing a portfolio of stocks using a trading agent is presented. A simulation game inspired by history-based Parrondo games is described. A performance measure is defined, with which various strategy mixes can be judged. Even when transaction costs are taken into account, active portfolio management (as opposed to Buy and Hold) is shown to be profitable.

  • 2. Andersson, Gerd
    et al.
    Bullock, Adrian
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Laaksolahti, Jarmo
    RISE, Swedish ICT, SICS, Computer Systems Laboratory.
    Nylander, Stina
    RISE, Swedish ICT, SICS, Software and Systems Engineering Laboratory.
    Olsson, Fredrik
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Sjölinder, Marie
    RISE, Swedish ICT, SICS.
    Waern, Annika
    RISE, Swedish ICT, SICS.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Classifying Mobile Services2004Report (Other academic)
    Abstract [en]

    A categorization of telecommunications services is presented, as a deliverable in a project commissioned by TeliaSonera.

    Download full text (pdf)
    FULLTEXT01
  • 3. Aurell, Erik
    et al.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Carlsson, Mats
    RISE, Swedish ICT, SICS, Computer Systems Laboratory.
    A trading agent built on constraint programming2002Conference paper (Refereed)
    Abstract [en]

    The Trading Agent Competition (TAC) combines a fairly realistic model of the Internet commerce of the future, including shopbots and pricebots, with a challenging problem in automated reasoning and decision making. Automated trading via auctions under severe time constraints are to be con-ducted by entering autonomous agents into TAC, assuming the role of travel agents. The TAC game rules, as well as a description of the discrete op-timization problem faced by an agent that wishes to allocate goods to its clients, are described. The TAC’01 entry “006”, encapsulating a constraint programming solution, is explained in some detail.

  • 4.
    Behravesh, Rasoul
    et al.
    Fdn Bruno Kessler, Digital Socity Ctr, SNESE Unit, Trento, Italy.;Univ Bologna, Dept Elect Elect & Informat Engn, I-40126 Bologna, Italy..
    Rao, Akhila
    Res Inst Sweden AB, Connected Intelligence, S-16440 Stockholm, Sweden..
    Perez-Ramirez, Daniel F.
    Res Inst Sweden AB, Connected Intelligence, S-16440 Stockholm, Sweden..
    Harutyunyan, Davit
    Robert Bosch GmbH, Corp Res, D-70465 Gerlingen, Germany..
    Riggio, Roberto
    Univ Politecn Marche, Informat Engn Dept, I-60121 Ancona, Italy..
    Boman, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming Over HTTP (DASH)2022In: IEEE Transactions on Network and Service Management, E-ISSN 1932-4537, Vol. 19, no 4, p. 4779-4793Article in journal (Refereed)
    Abstract [en]

    Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment's bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client's next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

  • 5. Bertels, K.
    et al.
    Jacques, J. -M
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV. Swedish Institute of Computer Science, Sweden.
    Risk and crises management in complex systems2005In: Micro Meso Macro: Addressing Complex Systems Couplings, World Scientific Publishing , 2005, p. 305-316Chapter in book (Refereed)
  • 6. Bertels, Koen
    et al.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Agent-based social simulation in markets2001In: Electronic Commerce Research, ISSN 1389-5753, E-ISSN 1572-9362, Vol. 1, no 1-2, p. 149-158Article in journal (Refereed)
    Abstract [en]

    We show that certain desired behavioural properties of agent-based models can be deterministically induced by an appropriate mathematical structure. We also point out problems related to the handling of parameters, and of the modelling of time, in agent-based models. Our purpose is to illustrate some problems of agent-based social simulations in markets, as a first step towards the more ambitious goal of providing a methodology for such simulations.

  • 7. Bertels, Koen
    et al.
    Jacques, Jean-Marie
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Risk and crises management in complex systems2005In: MICRO MESO MACRO: Addressing Complex Systems Couplings, World Scientific Publishing Company , 2005, 4, , p. 12p. 305-316Chapter in book (Refereed)
    Abstract [en]

    Many theories attempt to explain the nature of industrial and other kinds of hazards and some of the also stress the dynamical aspects of such events. We investigate to what extent the theory of self-organized criticality contributes to our understanding of industrial hazards.

  • 8.
    Boberg, Julia
    et al.
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Kaldo, Viktor
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden; Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden.
    Mataix-Cols, David
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Crowley, James J.
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden; Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.
    Roelstraete, Bjorn
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Halvorsen, Matthew
    Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.
    Forsell, Erik
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Isacsson, Nils H.
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Sullivan, Patrick F.
    Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
    Svanborg, Cecilia
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Andersson, Evelyn H.
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Lindefors, Nils
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Kravchenko, Olly
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Mattheisen, Manuel
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden; Department of Biomedicine, Aarhus University, Aarhus, Denmark.
    Danielsdottir, Hilda B.
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Ivanova, Ekaterina
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Boman, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Stockholm, Sweden.
    Fernández De La Cruz, Lorena
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Wallert, John
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Rück, Christian
    Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
    Swedish multimodal cohort of patients with anxiety or depression treated with internet-delivered psychotherapy (MULTI-PSYCH)2023In: BMJ Open, E-ISSN 2044-6055, Vol. 13, no 10, article id e069427Article in journal (Refereed)
    Abstract [en]

    Purpose Depression and anxiety afflict millions worldwide causing considerable disability. MULTI-PSYCH is a longitudinal cohort of genotyped and phenotyped individuals with depression or anxiety disorders who have undergone highly structured internet-based cognitive-behaviour therapy (ICBT). The overarching purpose of MULTI-PSYCH is to improve risk stratification, outcome prediction and secondary preventive interventions. MULTI-PSYCH is a precision medicine initiative that combines clinical, genetic and nationwide register data. Participants MULTI-PSYCH includes 2668 clinically well-characterised adults with major depressive disorder (MDD) (n=1300), social anxiety disorder (n=640) or panic disorder (n=728) assessed before, during and after 12 weeks of ICBT at the internet psychiatry clinic in Stockholm, Sweden. All patients have been blood sampled and genotyped. Clinical and genetic data have been linked to several Swedish registers containing a wide range of variables from patient birth up to 10 years after the end of ICBT. These variable types include perinatal complications, school grades, psychiatric and somatic comorbidity, dispensed medications, medical interventions and diagnoses, healthcare and social benefits, demographics, income and more. Long-term follow-up data will be collected through 2029. Findings to date Initial uses of MULTI-PSYCH include the discovery of an association between PRS for autism spectrum disorder and response to ICBT, the development of a machine learning model for baseline prediction of remission status after ICBT in MDD and data contributions to genome wide association studies for ICBT outcome. Other projects have been launched or are in the planning phase. Future plans The MULTI-PSYCH cohort provides a unique infrastructure to study not only predictors or short-term treatment outcomes, but also longer term medical and socioeconomic outcomes in patients treated with ICBT for depression or anxiety. MULTI-PSYCH is well positioned for research collaboration.

  • 9.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Artificial agent action in markets2001In: Electronic Commerce Research, ISSN 1389-5753, E-ISSN 1572-9362, Vol. 1, no 1-2, p. 159-168Article in journal (Refereed)
    Abstract [en]

    We summarise our experiences of a number of demonstrators and simulation experiments designed to test the feasibility of using artificial decision making agents in real-time domains, and comment on the significance of our results to autonomous artificial agent action patterns in markets. Our main hypothesis is that the use of norms can extend the capability of artificial decision makers beyond what is obtained from implementing individual utility maximisers in keeping with rational choice theory.

  • 10. Boman, Magnus
    Artificial Intelligence in Cities of the Future: Viable Cities Report 2019:12019Report (Other (popular science, discussion, etc.))
    Download full text (pdf)
    Artificial Intelligence
  • 11.
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture, Software and Computer Systems, SCS. Swedish Institute of Computer Science, Sweden.
    Commentary: The joy of mesh2009In: BMJ. British Medical Journal, ISSN 0959-8146, E-ISSN 0959-535X, Vol. 337, p. a2500-Article in journal (Refereed)
  • 12.
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Digital Cities: Strategic White Paper2013Report (Other (popular science, discussion, etc.))
  • 13.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    On Understanding Catastrophe — The Case of Highly Severe Influenza-Like Illness2011Conference paper (Refereed)
    Abstract [en]

    Computational epidemiology is a form of spatiotemporal reasoning in which social link structures are employed, and spatially explicit models are specified and executed. We point to issues thus far addressed neither by engineers, nor scientists, in the light of a use case focusing on catastrophic scenarios that assume the emergence of a highly unlikely but lethal and contagious strain of influenza. Our conclusion is that important perspectives are missing when dealing with policy issues resulting from scenario execution and analyses in computational epidemiology.

    Download full text (pdf)
    FULLTEXT01
  • 14.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Speedwriting in Networked Foresight2014Conference paper (Refereed)
  • 15.
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Speedwriting in Networked Foresight2014In: Innovation for Sustainable Economy & Society: The Proceedings of The XXV ISPIM Conference 2014 / [ed] Huizingh, K.R.E., Conn, S., Torkkeli, M. and Bitran, I., ISPIM Society , 2014Conference paper (Refereed)
    Abstract [en]

    Networked foresight is an established means to achieve an understanding of trends, changes, disruptives, and ideas with high innovation potential. When managed successfully, it allows for the elicitation of knowledge from competent professionals, with complementary resources, assets, and capabilities, providing benefit both to partners and to the network as a whole. The Innovation Radar business catalyst of EIT ICT Labs (a virtual organisation of multi-nationals, research institutes, and academic institutions) has used speedwriting as an integral part of its structured brainstorming, with the aim of efficiently producing networked foresight with adequate depth and width. Speedwriting aids width in particular, as it prompts the inclusion of disruptives and speculative developments. Eight Innovation Radar workshops involving more than 100 experts in total have employed the speedwriting element to maximise value for the organisation. Since speedwriting is a largely undocumented method, its merits to strategic and corporate foresight are here scrutinised in detail.

  • 16.
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Technical Foresight Report: Future Media Distribution2013Report (Other (popular science, discussion, etc.))
  • 17.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    The joy of mesh2008In: British Medical JournalArticle in journal (Refereed)
  • 18.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Trading agents2001In: AgentLink News, ISSN 1465-3842, Vol. 6, p. 15-17Article in journal (Other (popular science, discussion, etc.))
  • 19.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Who Were Where When? On the Use of Social Collective Intelligence in Computational Epidemiology2014In: Social Collective Intelligence, Switzerland: Springer , 2014, 9, p. 203-225Chapter in book (Refereed)
    Abstract [en]

    A triangular (case, theoretical, and literature) study approach is used to investigate if and how social collective intelligence is useful to computational epidemiology. The hypothesis is that the former can be employed for assisting in converting data into useful information through intelligent analyses by deploying new methods from data analytics that render previously unintelligible data intelligible. A conceptual bridge is built between the two concepts of crowd signals and syndromic surveillance. A concise list of empirical observations supporting the hypothesis is presented. The key observation is that new social collective intelligence methods and algorithms allow for massive data analytics to stay with the individual, in micro. It is thus possible to provide the analyst with advice tailored to the individual and with relevant policies, without resorting to macro (statistical) analyses of homogeneous populations.

  • 20.
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Who Were Where When?: On the Use of Social Collective Intelligence in Computational Epidemiology2014In: Social Collective Intelligence / [ed] Daniele Miorandi, Vincenzo Maltese, Michael Rovatsos, Anton Nijholt and James Stewart, Switzerland: Springer , 2014, p. 203-225Chapter in book (Refereed)
    Abstract [en]

    A triangular (case, theoretical, and literature) study approach is used to investigate if and how social collective intelligence is useful to computational epidemiology. The hypothesis is that the former can be employed for assisting in converting data into useful information through intelligent analyses by deploying new methods from data analytics that render previously unintelligible data intelligible. A conceptual bridge is built between the two concepts of crowd signals and syndromic surveillance. A concise list of empirical observations supporting the hypothesis is presented. The key observation is that new social collective intelligence methods and algorithms allow for massive data analytics to stay with the individual, in micro. It is thus possible to provide the analyst with advice tailored to the individual and with relevant policies, without resorting to macro (statistical) analyses of homogeneous populations.

  • 21.
    Boman, Magnus
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    ben Abdesslem, Fehmi
    Forsell, Erik
    Gillblad, Daniel
    Görnerup, Olof
    Isacsson, Nils
    Sahlgren, Magnus
    Kaldo, Viktor
    Learning machines in Internet-delivered psychological treatment2019In: Progress in Artificial Intelligence, ISSN 2192-6352, E-ISSN 2192-6360, Vol. 8, no 4, p. 475-485Article in journal (Refereed)
    Abstract [en]

    A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

  • 22.
    Boman, Magnus
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Forsell, Erik
    Karolinska Institute, Sweden; Stockholm County Council, Sweden.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Görnerup, Olof
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Isacsson, Nils
    Karolinska Institute, Sweden; Stockholm County Council, Sweden.
    Sahlgren, Magnus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Kaldo, Viktor
    Karolinska Institute, Sweden; Stockholm County Council, Sweden; Linnaeus University, Sweden.
    Learning machines in Internet-delivered psychological treatment2019In: Progress in Artificial Intelligence, ISSN 2192-6352, E-ISSN 2192-6360, Vol. 8, no 4, p. 475-485Article in journal (Refereed)
    Abstract [en]

    A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

    Download full text (pdf)
    fulltext
  • 23.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Bylund, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Espinoza, Fredrik
    RISE, Swedish ICT, SICS.
    Trading agents for roaming users2002Conference paper (Refereed)
  • 24.
    Boman, Magnus
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Cakici, Baki
    Stockholm University.
    Guttmann, Christian
    EBTIC.
    Al Hosani, Farida
    HAAD.
    Al Mannaei, Asma
    HAAD.
    Syndromic Surveillance in the United Arab Emirates2012In: International Conference on Innovations in Information Technology (IIT), 2012, IEEE conference proceedings, 2012, p. 31-35Conference paper (Refereed)
    Abstract [en]

    Opportunities for innovation in view of three complex problems faced by the UAE health care providers are described. The information dissemination problem faced could be approached by creating new channels for providing the population with public health information. These channels are precisely the ones typically used in so-called syndromic surveillance, including care-related data from communicable disease spread indicators, but also tweets and blog posts, for example. Syndromic surveillance could likewise assist the health authorities in addressing the knowledge elicitation problem: how to get more information on the life style, self care, and prevention among individual citizens. To some extent the prediction problem—how to predict the spread of infectious disease in the future and how to mathematically model social behaviour in the case of various health-threatening scenarios—would also be addressed by syndromic surveillance. Fully employed, the solutions proposed would provide new ICT services enabling preparedness for many forms of communicable disease outbreaks, as well as for natural disasters.

    Download full text (pdf)
    fulltext
  • 25.
    Boman, Magnus
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Downs, Johnny
    Kings Coll London, Natl Inst Hlth Res, Maudsley Biomed Res Ctr, Child & Adolescent Psychiat Psychol Med & Integra, London, England..
    Karali, Abubakrelsedik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. NVIDIA Corp, London, England..
    Pawlby, Susan
    South London & Maudsley Natl Hlth Serv Trust, Bethlem Royal Hosp, Channi Kumar Mother & Baby Unit, London, England..
    Toward Learning Machines at a Mother and Baby Unit2020In: Frontiers in Psychology, E-ISSN 1664-1078, Vol. 11, article id 567310Article in journal (Refereed)
    Abstract [en]

    Agnostic analyses of unique video material from a Mother and Baby Unit were carried out to investigate the usefulness of such analyses to the unit. The goal was to improve outcomes: the health of mothers and their babies. The method was to implement a learning machine that becomes more useful over time and over task. A feasible set-up is here described, with the purpose of producing intelligible and useful results to healthcare professionals at the unit by means of a vision processing pipeline, grouped together with multi-modal capabilities of handling annotations and audio. Algorithmic bias turned out to be an obstacle that could only partly be handled by modern pipelines for automated feature analysis. The professional use of complex quantitative scoring for various mental health-related assessments further complicated the automation of laborious tasks. Activities during the MBU stay had previously been shown to decrease psychiatric symptoms across diagnostic groups. The implementation and first set of experiments on a learning machine for the unit produced the first steps toward explaining why this is so, in turn enabling decision support to staff about what to do more and what to do less of.

  • 26.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Espinoza, Fredrik
    RISE, Swedish ICT, SICS.
    Franzén, Kristofer
    RISE, Swedish ICT, SICS.
    Hansen, Preben
    RISE, Swedish ICT, SICS.
    Bylund, Markus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Svensson, Martin
    RISE, Swedish ICT, SICS.
    Human Grid: En förstudie2007Report (Other academic)
    Abstract [sv]

    Vi har granskat förutsättningarna och möjligheterna att implementera Human Grid: en så kallad mellanprogramvara för att integrera samarbetsfrämjande IT-lösningar som redan idag finns i datorer och telefoner, med hänsyn tagen till formella och informella sociala nätverk.

    Download full text (pdf)
    FULLTEXT01
  • 27.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ghaffar, Asim
    Liljeros, Fredrik
    Stenhem, Mikael
    Social network visualization as a contract tracing tool2006Conference paper (Refereed)
    Abstract [en]

    Something many pathogens have in common is the requirement for tracing their spread under harsh time constraints, posing a so-called contact tracing (or ``race-to-trace'') problem. We present a tool for visualizing contact networks, an important step towards practical use by epidemiologists, which generates interactive three-dimensional (3D) network visualizations. Its general purpose visualization engine can support multiple applications and varying pathogens. The main purpose is to trace, in the case of an outbreak, contacts among individuals known to have been at the same place.

  • 28.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Gillblad, Daniel
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Learning machines for computational epidemiology2014Conference paper (Refereed)
    Abstract [en]

    Resting on our experience of computational epidemiology in practice and of industrial projects on analytics of complex networks, we point to an innovation opportunity for improving the digital services to epidemiologists for monitoring, modeling, and mitigating the effects of communicable disease. Artificial intelligence and intelligent analytics of syndromic surveillance data promise new insights to epidemiologists, but the real value can only be realized if human assessments are paired with assessments made by machines. Neither massive data itself, nor careful analytics will necessarily lead to better informed decisions. The process producing feedback to humans on decision making informed by machines can be reversed to consider feedback to machines on decision making informed by humans, enabling learning machines. We predict and argue for the fact that the sensemaking that such machines can perform in tandem with humans can be of immense value to epidemiologists in the future.

  • 29.
    Boman, Magnus
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. SICS.
    Gillblad, Daniel
    SICS.
    Learning Machines for Computational Epidemiology2014In: Proceedings - 2014 IEEE International Conference on Big Data, Washington DC: IEEE conference proceedings, 2014, p. 1-5Conference paper (Refereed)
    Abstract [en]

    Resting on our experience of computational epidemiologyin practice and of industrial projects on analytics ofcomplex networks, we point to an innovation opportunity forimproving the digital services to epidemiologists for monitoring,modeling, and mitigating the effects of communicable disease.Artificial intelligence and intelligent analytics of syndromicsurveillance data promise new insights to epidemiologists, butthe real value can only be realized if human assessments arepaired with assessments made by machines. Neither massivedata itself, nor careful analytics will necessarily lead to betterinformed decisions. The process producing feedback to humanson decision making informed by machines can be reversed toconsider feedback to machines on decision making informed byhumans, enabling learning machines. We predict and argue forthe fact that the sensemaking that such machines can perform intandem with humans can be of immense value to epidemiologistsin the future.

  • 30.
    Boman, Magnus
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Gullström, Charlie
    KTH, School of Architecture and the Built Environment (ABE), Architecture.
    Mediated Futures: Technical Foresight report. European Institute of Innovation and Technology, EIT ICT Labs2015Report (Other academic)
    Abstract [en]

    This report outlines trends, challenges, and opportunities relating to the future of

    Smart Spaces and ICT-mediated human communication, as observed from within

    one of the EIT ICT Labs focus areas: Mediating Presence, during 2012-2013. The

    study should be seen as an initial and open-ended exploration that seeks to

    contribute a productive point of departure for more ambitious work, which will be

    undertaken across the Smart Spaces Action Line and using the Innovation Radar

    platform in future years. In particular, the business potential of mediating presence is

    the focus of a forthcoming 2014 Foresight Technical Report.

    As a foresight, Mediated Futures identifies and exposes future themes with high

    innovation potential relating to presence technologies, using a time frame roughly six

    months to five years ahead. Its purpose is to create a common outlook on the future

    of ICT and to establish a shared vocabulary and fruitful methodologies for future

    strategy thinking across the EIT ICT Labs nodes and partner organisations.

    A series of workshops and other collaborative activities have been organised within

    the Mediating Presence activity over the last 15 months. The pivotal output is a

    series of one-pagers, short fictional texts, three of which can be encountered on the

    following pages. Tentative and possibly provocative, these are slogan-based

    descriptions of future scenarios that serve to trigger new perspectives. A total of six

    clusters of topics were covered by one-pagers:

     Data doubles

     New magic

     Luddites

     Socialites

     Future of WorkA working future

     Spaces and things

  • 31.
    Boman, Magnus
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
    Heger, Tobias
    Circles of Impression: External Foresight in Global Enterprises2019In: Futures Thinking and Organizational Policy / [ed] D. A. Schreiber and Z. L. Berge, Palgrave Macmillan, 2019, p. 179-199Chapter in book (Refereed)
  • 32.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Holm, Einar
    Multi-agent systems, time geography, and microsimulations2004In: Systems Approaches and Their Application, Kluwer , 2004, 1, , p. 340p. 95-118Chapter in book (Refereed)
    Abstract [en]

    In this chapter we consider the role virtual conferencing has to play in realising a successful Inhabited Information Space (IIS). For any IIS to be successful it needs to weave together many different constituent elements and present these in a coherent and seamless manner. For example, Maher et al (2000) describe how many different components are used together to create a virtual design studio for architectural collaboration. For the IIS to function all the elements must work both individually and collectively. Communication is one of the basic building blocks for an IIS, and can be in many modes across many media. Approaches to virtual conferencing offer support for communication across a number of media and can be utilised in an IIS. These approaches are also starting to additionally offer support for collaboration. By providing an introduction to and overview of various possibilities for virtual conferencing we aim to show how these solutions can provide the required and appropriate support for communication and collaboration between inhabitants in a shared information space. Of course virtual conferencing solutions exist at many levels of sophistication and fidelity. Communication media can range from text through 3d graphics to video representations. The aim of this chapter is to present these many and varied possibilities, drawing on the experience of the author as well as insights into the past, present and future. In this way it is possible to see how diverse a range of IISes can make use of virtual conferencing functionality.

  • 33.
    Boman, Magnus
    et al.
    Swedish Institute of Computer Science (SICS).
    Johansson, S. J.
    Modeling epidemic spread in synthetic populations - Virtual plagues in Massively Multiplayer Online Games2007In: 3rd Digital Games Research Association International Conference: "Situated Play", DiGRA 2007, 2007, p. 357-361Conference paper (Refereed)
    Abstract [en]

    A virtual plague is a process in which a behavior-affecting property spreads among characters in a Massively Multiplayer Online Game (MMOG). The MMOG individuals constitute a synthetic population, and the game can be seen as a form of interactive executable model for studying disease spread, albeit of a very special kind. To a game developer maintaining an MMOG, recognizing, monitoring, and ultimately controlling a virtual plague is important, regardless of how it was initiated. The prospect of using tools, methods and theory from the field of epidemiology to do this seems natural and appealing. We will address the feasibility of such a prospect, first by considering some basic measures used in epidemiology, then by pointing out the differences between real world epidemics and virtual plagues. We also suggest directions for MMOG developer control through epidemiological modeling. Our aim is understanding the properties of virtual plagues, rather than trying to eliminate them or mitigate their effects, as would be in the case of real infectious disease.

  • 34.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Johansson, Stefan J.
    Modeling Epidemic Spread in Synthetic Populations - Virtual Plagues in Massively Multiplayer Online Games2007Conference paper (Refereed)
    Abstract [en]

    A virtual plague is a process in which a behavior-affecting property spreads among characters in a Massively Multiplayer Online Game (MMOG). The MMOG individuals constitute a synthetic population, and the game can be seen as a form of interactive executable model for studying disease spread, albeit of a very special kind. To a game developer maintaining an MMOG, recognizing, monitoring, and ultimately controlling a virtual plague is important, regardless of how it was initiated. The prospect of using tools, methods and theory from the field of epidemiology to do this seems natural and appealing. We will address the feasibility of such a prospect, first by considering some basic measures used in epidemiology, then by pointing out the differences between real world epidemics and virtual plagues. We also suggest directions for MMOG developer control through epidemiological modeling. Our aim is understanding the properties of virtual plagues, rather than trying to eliminate them or mitigate their effects, as would be in the case of real infectious disease.

  • 35.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Johansson, Stefan
    Lybäck, David
    Parrondo strategies for artificial traders2001In: Intelligent Agent Technology, World Scientific, 2001, 1, , p. 10p. 150-159Chapter in book (Refereed)
    Abstract [en]

    On markets with receding prices, artificial noise traders may consider alternatives to buy-and-hold. By simulating variations of the Parrondo strategy, using real data from the Swedish stock market, we produce first indications of a buy-low-sell-random Parrondo variation outperforming buy-and-hold. Subject to our assumptions, buy-low-sell-random also outperforms the traditional value and trend investor strategies. We measure the success of the Parrondo variations not only through their performance compared to other kinds of strategies, but also relative to varying levels of perfect information, received through messages within a multi-agent system of artificial traders.

  • 36.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS.
    Karlgren, Jussi
    RISE, Swedish ICT, SICS.
    Abstrakta maskiner och formella språk1996 (ed. 1)Book (Refereed)
    Abstract [sv]

    Lärobok i formella språk.

  • 37. Boman, Magnus
    et al.
    Kordas, Olga
    SWOT-analys av hur artificiell intelligens och maskininlärning påverkar Viable Cities: April 2018 – Viable Cities info 2018:12018Report (Other (popular science, discussion, etc.))
    Abstract [sv]

    Detta dokument har tagits fram som underlag till Vinnovas uppdrag från regeringen att genomföra en kartläggning och analys av hur väl artificiell intelligens och maskininlärning kommer till användning i svensk industri och i det svenska samhället.

    Download full text (pdf)
    SWOT-analys
  • 38.
    Boman, Magnus
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. RISE SICS AB, Sweden.
    Kruse, E.
    Supporting global health goals with information and communications technology2017In: Global Health Action, ISSN 1654-9716, E-ISSN 1654-9880, Vol. 10, article id 1321904Article in journal (Refereed)
    Abstract [en]

    The objective of this study is to critically assess the possible roles of information and communications technology (ICT) in supporting global health goals. This is done by considering privilege and connectibility. In short, ICT can contribute by providing health information via four different kinds of access, each with its own history and prospective future. All four are analyzed here, in two perspectives: business-as-usual and disruptive. Health data analytics is difficult since the digital representation of past, current, and future health information is lacking. The flow of analytics that may prove beneficial to the individual and not just meet abstract population-level goals or ambitions is analyzed in detail. Sensemaking is also needed, to meet the minimum requirement of making prospective future services understandable to policymakers. Drivers as well as barriers for areas in which policy decisions have the potential to drive positive developments for meeting the Sustainable Development Goals are identified.

  • 39.
    Boman, Magnus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Kruse, Erik
    Ericsson, Sweden.
    Supporting global health goals with information and communications technology2017In: Global Health Action, ISSN 1654-9716, E-ISSN 1654-9880, Vol. 10, article id 1321904Article in journal (Refereed)
    Abstract [en]

    The objective of this study is to critically assess the possible roles of information and communications technology (ICT) in supporting global health goals. This is done by considering privilege and connectibility. In short, ICT can contribute by providing health information via four different kinds of access, each with its own history and prospective future. All four are analyzed here, in two perspectives: business-as-usual and disruptive. Health data analytics is difficult since the digital representation of past, current, and future health information is lacking. The flow of analytics that may prove beneficial to the individual and not just meet abstract population-level goals or ambitions is analyzed in detail. Sensemaking is also needed, to meet the minimum requirement of making prospective future services understandable to policymakers. Drivers as well as barriers for areas in which policy decisions have the potential to drive positive developments for meeting the Sustainable Development Goals are identified. © 2017 The Author(s).

    Download full text (pdf)
    fulltext
  • 40.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Laaksolahti, Jarmo
    RISE, Swedish ICT, SICS, Computer Systems Laboratory.
    Espinoza, Fredrik
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Cöster, Rickard
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Trust in Micro Service Environments2006Report (Other academic)
    Abstract [en]

    Report produced in the project Enabling and Promoting Trust in Micro Service Environments (EPTMSE) with a web site at www.trust-eze.org. The report gives an overview of the concept of trust in domains such as psychology, sociology, philosophy, and computer science, and then describes the current domain of Micro Service Environments - open and unregulated electronic service environments - where users can create, use, and share electronic services, and where the need for decentralized trust mechanisms is high. Some design and implementation choices and solutions for trust mechanisms are suggested.

    Download full text (pdf)
    FULLTEXT01
  • 41.
    Boman, Magnus
    et al.
    Swedish Institute of Computer Science, RISE SICS.
    Sahlgren, Magnus
    Swedish Institute of Computer Science, RISE SICS.
    Görnerup, Olof
    Swedish Institute of Computer Science, RISE SICS.
    Gillblad, Daniel
    Swedish Institute of Computer Science, RISE SICS.
    Learning Machines2018In: Learning, Inference and Control of Multi-Agent Systems, 2018, p. 610-613Conference paper (Refereed)
  • 42.
    Boman, Magnus
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Sanches, Pedro
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Sensemaking in Intelligent Data Analytics2015In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 29, no 2, p. 143-152Article in journal (Refereed)
    Abstract [en]

    A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.

  • 43.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab. KTH Royal Institute of Technology, Sweden.
    Sanches, Pedro
    RISE, Swedish ICT, SICS.
    Sensemaking in Intelligent Health Data Analytics2015In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 29, no 2, p. 143-152Article in journal (Refereed)
    Abstract [en]

    A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.

  • 44.
    Boman, Magnus
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Sandin, Anna
    Implementing an agent trade server2004In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797Article in journal (Refereed)
    Abstract [en]

    An experimental server for stock trading autonomous agents is presented and made available, together with an agent shell for swift development. The server, written in Java, was implemented as proof-of-concept for an agent trade server for a real financial exchange.

  • 45.
    Boman, Magnus
    et al.
    KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Sandin, Anna
    Implementing an agent trade server2006In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 42, no 1, p. 318-327Article in journal (Refereed)
    Abstract [en]

    An experimental server for stock trading autonomous agents is presented and made available, together with an agent shell for swift development. The server, written in Java, was implemented as proof-of-concept for an agent trade server for a real financial exchange.

  • 46. Borg Gyllenbäck, Katarina
    et al.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Narrative Bridging2011In: Design Computing and Cognition '10, Springer , 2011, 7, p. 525-544Chapter in book (Refereed)
    Abstract [en]

    In the design of interactive media, various forms of intuitive practice come into play. It might prove tempting to use templates and strong narrative structures from film, instead of developing the narrative directly for interactive media, leading to a move towards computer implementation too swiftly. The narrative bridging method focuses on the initial design phase, in which the conceptual modeling takes place. The purpose is to provide designers with a non-intrusive method that aids the design without interfering with creativity. The method supports the sentient construction of digital games with a narrative, with the ultimate goal of enhancing the player’s experience. A prototype test served as a first evaluation, and two games from that test are showcased here for the purpose of illustrating the hands-on use of narrative bridging. The test demonstrated that the method could aid time-constrained design, and in the process detect inconsistencies that could prevent the design team from making improvements. The method also provided teams with a shared vocabulary and outlook.

  • 47.
    Borlenghi, Simone
    et al.
    KTH, School of Engineering Sciences (SCI), Applied Physics, Materials and Nanophysics.
    Boman, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS. RISE SICS, Electrum 229, SE-16429 Kista, Sweden..
    Delin, Anna
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Material Physics. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Modeling reservoir computing with the discrete nonlinear Schrodinger equation2018In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 98, no 5, article id 052101Article in journal (Refereed)
    Abstract [en]

    We formulate, using the discrete nonlinear Schrodinger equation (DNLS), a general approach to encode and process information based on reservoir computing. Reservoir computing is a promising avenue for realizing neuromorphic computing devices. In such computing systems, training is performed only at the output level by adjusting the output from the reservoir with respect to a target signal. In our formulation, the reservoir can be an arbitrary physical system, driven out of thermal equilibrium by an external driving. The DNLS is a general oscillator model with broad application in physics, and we argue that our approach is completely general and does not depend on the physical realization of the reservoir. The driving, which encodes the object to be recognized, acts as a thermodynamic force, one for each node in the reservoir. Currents associated with these thermodynamic forces in turn encode the output signal from the reservoir. As an example, we consider numerically the problem of supervised learning for pattern recognition, using as a reservoir a network of nonlinear oscillators.

  • 48.
    Borlenghi, Simone
    et al.
    KTH Royal Institute of Technology, Sweden.
    Boman, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Delin, Anna
    KTH Royal Institute of Technology, Sweden.
    Modeling reservoir computing with the discrete nonlinear Schrödinger equation2018In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 98, no 5, article id 052101Article in journal (Refereed)
    Abstract [en]

    We formulate, using the discrete nonlinear Schrödinger equation (DNLS), a general approach to encode and process information based on reservoir computing. Reservoir computing is a promising avenue for realizing neuromorphic computing devices. In such computing systems, training is performed only at the output level by adjusting the output from the reservoir with respect to a target signal. In our formulation, the reservoir can be an arbitrary physical system, driven out of thermal equilibrium by an external driving. The DNLS is a general oscillator model with broad application in physics, and we argue that our approach is completely general and does not depend on the physical realization of the reservoir. The driving, which encodes the object to be recognized, acts as a thermodynamic force, one for each node in the reservoir. Currents associated with these thermodynamic forces in turn encode the output signal from the reservoir. As an example, we consider numerically the problem of supervised learning for pattern recognition, using as a reservoir a network of nonlinear oscillators.

  • 49. Brouwers, Lisa
    et al.
    Boman, Magnus
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A Computational Agent Model of Flood Management Strategies2011In: Computational Methods Applied to Agricultural Research: Advances and Applications, IGI Global , 2011, 7, p. 296-307Chapter in book (Refereed)
    Abstract [en]

    A geographically explicit flood simulation model was designed and implemented as a tool for policy making support, illustrated here with two simple flood management strategies pertaining to the Upper Tisza area in Hungary. The model integrates aspects of the geographical, hydrological, economical, land use, and social context. The perspectives of different stakeholders are represented as agents that make decisions on whether or not to buy flood insurance. We demonstrate that agent-based models can be important for policy issues in general, and for sustainable development policy issues in particular, by aiding stakeholder communication and learning, thereby increasing the chances of reaching robust decisions. The agent-based approach enables the highlighting and communication of distributional effects of policy changes at the micro-level, as illustrated by several graphical representations of outputs from the model.

  • 50.
    Brouwers, Lisa
    et al.
    KTH, School of Information and Communication Technology (ICT).
    Boman, Magnus
    KTH, School of Information and Communication Technology (ICT). The Swedish Institute of Computer Science (SICS), Sweden.
    A computational agent model of flood management strategies2010In: Computational Methods for Agricultural Research: Advances and Applications, IGI Global , 2010, p. 296-307Chapter in book (Other academic)
    Abstract [en]

    A geographically explicit flood simulation model was designed and implemented as a tool for policy making support, illustrated here with two simple flood management strategies pertaining to the Upper Tisza area in Hungary. The model integrates aspects of the geographical, hydrological, economical, land use, and social context. The perspectives of different stakeholders are represented as agents that make decisions on whether or not to buy flood insurance. The authors demonstrate that agent-based models can be important for policy issues in general, and for sustainable development policy issues in particular, by aiding stakeholder communication and learning, thereby increasing the chances of reaching robust decisions. The agent-based approach enables the highlighting and communication of distributional effects of policy changes at the micro-level, as illustrated by several graphical representations of outputs from the model.

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