83 research outputs found

    Participatory development of decision support systems: which features of the process lead to improved uptake and better outcomes?

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    Decision support systems (DSSs) are important in decision-making environments with conflicting interests. Many DSSs developed have not been used in practice. Experts argue that these tools do not respond to real user needs and that the inclusion of stakeholders in the development process is the solution. However, it is not clear which features of participatory development of DSSs result in improved uptake and better outcomes. A review of papers, reporting on case studies where DSSs and other decision tools (information systems, software and scenario tools) were developed with elements of participation, was carried out. The cases were analysed according to a framework created as part of this research; it includes criteria to evaluate the development process and the outcomes. Relevant aspects to consider in the participatory development processes include establishing clear objectives, timing and location of the process; keeping discussions on track; favouring participation and interaction of individuals and groups; and challenging creative thinking of the tool and future scenarios. The case studies that address these issues show better outcomes; however, there is a large degree of uncertainty concerning them because developers have typically neither asked participants about their perceptions of the processes and resultant tools nor have they monitored the use and legacy of the tools over the long term.The authors would like to thank COST Action FP0804-Forest Management Decision Support Systems (FORSYS) for financing a three month Short-Term Scientific Mission (STSM) in Forest Research (Roslin, UK) in 2012, making possible this research; Spanish Ministry of Economy and Competitiveness for supporting the project Multicriteria Techniques and Participatory Decision-Making for Sustainable Management (Ref. ECO2011-27369) where the leading author is involved; and the Regional Ministry of Education, Culture and Sports (Valencia, Spain) for financing a research fellowship (Ref. ACIF/2010/248).Valls Donderis, P.; Ray, D.; Peace, A.; Stewart, A.; Lawrence, A.; Galiana, F. (2013). Participatory development of decision support systems: which features of the process lead to improved uptake and better outcomes?. Scandinavian Journal of Forest Research. 29(1):71-83. https://doi.org/10.1080/02827581.2013.837950S7183291Arnstein, S. R. (1969). A Ladder Of Citizen Participation. Journal of the American Institute of Planners, 35(4), 216-224. doi:10.1080/01944366908977225Atwell, R. C., Schulte, L. A., & Westphal, L. M. (2011). Tweak, Adapt, or Transform: Policy Scenarios in Response to Emerging Bioenergy Markets in the U.S. Corn Belt. Ecology and Society, 16(1). doi:10.5751/es-03854-160110Barac, A., Kellner, K., & De Klerk, N. (2004). Land User Participation in Developing a Computerised Decision Support System for Combating Desertification. Environmental Monitoring and Assessment, 99(1-3), 223-231. doi:10.1007/s10661-004-4022-6Bennet, A., & Bennet, D. (2008). The Decision-Making Process in a Complex Situation. Handbook on Decision Support Systems 1, 3-20. doi:10.1007/978-3-540-48713-5_1Blackstock, K. L., Kelly, G. J., & Horsey, B. L. (2007). Developing and applying a framework to evaluate participatory research for sustainability. Ecological Economics, 60(4), 726-742. doi:10.1016/j.ecolecon.2006.05.014Breuer, N. E., Cabrera, V. E., Ingram, K. T., Broad, K., & Hildebrand, P. E. (2007). AgClimate: a case study in participatory decision support system development. Climatic Change, 87(3-4), 385-403. doi:10.1007/s10584-007-9323-7Bunch, M. J., & Dudycha, D. J. (2004). Linking conceptual and simulation models of the Cooum River: collaborative development of a GIS-based DSS for environmental management. Computers, Environment and Urban Systems, 28(3), 247-264. doi:10.1016/s0198-9715(03)00021-8Byrne, E., & Sahay, S. (2007). Participatory design for social development: A South African case study on community-based health information systems. Information Technology for Development, 13(1), 71-94. doi:10.1002/itdj.20052Cain, J. ., Jinapala, K., Makin, I. ., Somaratna, P. ., Ariyaratna, B. ., & Perera, L. . (2003). Participatory decision support for agricultural management. A case study from Sri Lanka. Agricultural Systems, 76(2), 457-482. doi:10.1016/s0308-521x(02)00006-9Chakraborty, A. (2011). Enhancing the role of participatory scenario planning processes: Lessons from Reality Check exercises. Futures, 43(4), 387-399. doi:10.1016/j.futures.2011.01.004Cinderby, S., Bruin, A. de, Mbilinyi, B., Kongo, V., & Barron, J. (2011). Participatory geographic information systems for agricultural water management scenario development: A Tanzanian case study. Physics and Chemistry of the Earth, Parts A/B/C, 36(14-15), 1093-1102. doi:10.1016/j.pce.2011.07.039Drew, C. H., Nyerges, T. L., & Leschine, T. M. (2004). Promoting Transparency of Long‐Term Environmental Decisions: The Hanford Decision Mapping System Pilot Project. Risk Analysis, 24(6), 1641-1664. doi:10.1111/j.0272-4332.2004.00556.xDriedger, S. M., Kothari, A., Morrison, J., Sawada, M., Crighton, E. J., & Graham, I. D. (2007). Using participatory design to develop (public) health decision support systems through GIS. International Journal of Health Geographics, 6(1), 53. doi:10.1186/1476-072x-6-53Evers, M. (2008). An analysis of the requirements for DSS on integrated river basin management. Management of Environmental Quality: An International Journal, 19(1), 37-53. doi:10.1108/14777830810840354Iivari, N. (2011). Participatory design in OSS development: interpretive case studies in company and community OSS development contexts. Behaviour & Information Technology, 30(3), 309-323. doi:10.1080/0144929x.2010.503351Innes, J. E., & Booher, D. E. (1999). Consensus Building and Complex Adaptive Systems. Journal of the American Planning Association, 65(4), 412-423. doi:10.1080/01944369908976071Jakku, E., & Thorburn, P. J. (2010). A conceptual framework for guiding the participatory development of agricultural decision support systems. Agricultural Systems, 103(9), 675-682. doi:10.1016/j.agsy.2010.08.007Jessel, B., & Jacobs, J. (2005). Land use scenario development and stakeholder involvement as tools for watershed management within the Havel River Basin. Limnologica, 35(3), 220-233. doi:10.1016/j.limno.2005.06.006Kautz, K. (2011). Investigating the design process: participatory design in agile software development. Information Technology & People, 24(3), 217-235. doi:10.1108/09593841111158356Kowalski, K., Stagl, S., Madlener, R., & Omann, I. (2009). Sustainable energy futures: Methodological challenges in combining scenarios and participatory multi-criteria analysis. European Journal of Operational Research, 197(3), 1063-1074. doi:10.1016/j.ejor.2007.12.049Lawrence, A. (2006). ‘No Personal Motive?’ Volunteers, Biodiversity, and the False Dichotomies of Participation. Ethics, Place & Environment, 9(3), 279-298. doi:10.1080/13668790600893319Mao, J., & Song, W. (2008). Empirical study of distinct features and challenges of joint development of information systems: The case of ABC bank. Tsinghua Science and Technology, 13(3), 414-419. doi:10.1016/s1007-0214(08)70066-xMenzel, S., Nordström, E.-M., Buchecker, M., Marques, A., Saarikoski, H., & Kangas, A. (2012). Decision support systems in forest management: requirements from a participatory planning perspective. European Journal of Forest Research, 131(5), 1367-1379. doi:10.1007/s10342-012-0604-yMoote, M. A., Mcclaran, M. P., & Chickering, D. K. (1997). RESEARCH: Theory in Practice: Applying Participatory Democracy Theory to Public Land Planning. Environmental Management, 21(6), 877-889. doi:10.1007/s002679900074Peleg, M., Shachak, A., Wang, D., & Karnieli, E. (2009). Using multi-perspective methodologies to study users’ interactions with the prototype front end of a guideline-based decision support system for diabetic foot care. International Journal of Medical Informatics, 78(7), 482-493. doi:10.1016/j.ijmedinf.2009.02.008Pretty, J. N. (1995). Participatory learning for sustainable agriculture. World Development, 23(8), 1247-1263. doi:10.1016/0305-750x(95)00046-fReed MS. 2008. Stakeholder participation for environmental management: a literature review. Sustainability Research Institute, School of Earth and Environment, University of Leeds.Reed, M. S., & Dougill, A. J. (2010). Linking degradation assessment to sustainable land management: A decision support system for Kalahari pastoralists. Journal of Arid Environments, 74(1), 149-155. doi:10.1016/j.jaridenv.2009.06.016Rowe, G., & Frewer, L. J. (2000). Public Participation Methods: A Framework for Evaluation. Science, Technology, & Human Values, 25(1), 3-29. doi:10.1177/016224390002500101Schielen, R. M. J., & Gijsbers, P. J. A. (2003). DSS-large rivers: developing a DSS under changing societal requirements. Physics and Chemistry of the Earth, Parts A/B/C, 28(14-15), 635-645. doi:10.1016/s1474-7065(03)00109-8Sheppard, S. R. J., & Meitner, M. (2005). Using multi-criteria analysis and visualisation for sustainable forest management planning with stakeholder groups. Forest Ecology and Management, 207(1-2), 171-187. doi:10.1016/j.foreco.2004.10.032Thursky, K. A., & Mahemoff, M. (2007). User-centered design techniques for a computerised antibiotic decision support system in an intensive care unit. International Journal of Medical Informatics, 76(10), 760-768. doi:10.1016/j.ijmedinf.2006.07.011Webler, S. T., Thomas. (1999). Voices from the Forest: What Participants Expect of a Public Participation Process. Society & Natural Resources, 12(5), 437-453. doi:10.1080/089419299279524Van Meensel, J., Lauwers, L., Kempen, I., Dessein, J., & Van Huylenbroeck, G. (2012). Effect of a participatory approach on the successful development of agricultural decision support systems: The case of Pigs2win. Decision Support Systems, 54(1), 164-172. doi:10.1016/j.dss.2012.05.002Von Geibler, J., Kristof, K., & Bienge, K. (2010). Sustainability assessment of entire forest value chains: Integrating stakeholder perspectives and indicators in decision support tools. Ecological Modelling, 221(18), 2206-2214. doi:10.1016/j.ecolmodel.2010.03.02

    Ecological expected utility and the mythical neural code

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    Neural spikes are an evolutionarily ancient innovation that remains nature’s unique mechanism for rapid, long distance information transfer. It is now known that neural spikes sub serve a wide variety of functions and essentially all of the basic questions about the communication role of spikes have been answered. Current efforts focus on the neural communication of probabilities and utility values involved in decision making. Significant progress is being made, but many framing issues remain. One basic problem is that the metaphor of a neural code suggests a communication network rather than a recurrent computational system like the real brain. We propose studying the various manifestations of neural spike signaling as adaptations that optimize a utility function called ecological expected utility

    The Complex Genetic Architecture of the Metabolome

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    Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable

    A Genome-Wide Association Study Identifies Variants Underlying the Arabidopsis thaliana Shade Avoidance Response

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    Shade avoidance is an ecologically and molecularly well-understood set of plant developmental responses that occur when the ratio of red to far-red light (R∶FR) is reduced as a result of foliar shade. Here, a genome-wide association study (GWAS) in Arabidopsis thaliana was used to identify variants underlying one of these responses: increased hypocotyl elongation. Four hypocotyl phenotypes were included in the study, including height in high R∶FR conditions (simulated sun), height in low R∶FR conditions (simulated shade), and two different indices of the response of height to low R∶FR. GWAS results showed that variation in these traits is controlled by many loci of small to moderate effect. A known PHYC variant contributing to hypocotyl height variation was identified and lists of significantly associated genes were enriched in a priori candidates, suggesting that this GWAS was capable of generating meaningful results. Using metadata such as expression data, GO terms, and other annotation, we were also able to identify variants in candidate de novo genes. Patterns of significance among our four phenotypes allowed us to categorize associations into three groups: those that affected hypocotyl height without influencing shade avoidance, those that affected shade avoidance in a height-dependent fashion, and those that exerted specific control over shade avoidance. This grouping allowed for the development of explicit hypotheses about the genetics underlying shade avoidance variation. Additionally, the response to shade did not exhibit any marked geographic distribution, suggesting that variation in low R∶FR–induced hypocotyl elongation may represent a response to local conditions

    Adaptive Value of Phenological Traits in Stressful Environments: Predictions Based on Seed Production and Laboratory Natural Selection

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    Phenological traits often show variation within and among natural populations of annual plants. Nevertheless, the adaptive value of post-anthesis traits is seldom tested. In this study, we estimated the adaptive values of pre- and post-anthesis traits in two stressful environments (water stress and interspecific competition), using the selfing annual species Arabidopsis thaliana. By estimating seed production and by performing laboratory natural selection (LNS), we assessed the strength and nature (directional, disruptive and stabilizing) of selection acting on phenological traits in A. thaliana under the two tested stress conditions, each with four intensities. Both the type of stress and its intensity affected the strength and nature of selection, as did genetic constraints among phenological traits. Under water stress, both experimental approaches demonstrated directional selection for a shorter life cycle, although bolting time imposes a genetic constraint on the length of the interval between bolting and anthesis. Under interspecific competition, results from the two experimental approaches showed discrepancies. Estimation of seed production predicted directional selection toward early pre-anthesis traits and long post-anthesis periods. In contrast, the LNS approach suggested neutrality for all phenological traits. This study opens questions on adaptation in complex natural environment where many selective pressures act simultaneously

    The Nature of Knowledge in Composition and Literary Understanding: The Question of Specificity

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    ↵PETER SMAGORINSKY is Assistant Professor, College of Education, University of Oklahoma, 820 Van Vleet Oval, Norman, OK 73019-0. He specializes in classroom literacy.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline
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