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Asian Bioethics Review

, Volume 10, Issue 4, pp 295–312 | Cite as

On the Ethics of Biodiversity Models, Forecasts and Scenarios

  • Pierre MazzegaEmail author
Original Paper

Abstract

The development of numerical models to produce realistic prospective scenarios for the evolution of biological diversity is essential. Only integrative impact assessment models are able to take into account the diverse and complex interactions embedded in social-ecological systems. The knowledge used is objective, the procedure of their integration is rigorous and the data massive. Nevertheless, the technical choices (model ontology, treatment of scales and uncertainty, data choice and pre-processing, technique of representation, etc.) made at each stage of the development of models and scenarios are mostly circumstantial, depending on both the skills of modellers on a project and the means available to them. In the end, the scenarios selected and the way they are simulated limit the futures explored, and the options offered to decision makers and stakeholders to act. The ethical implications of these circumstantial choices are generally not documented, explained or even perceived by modellers. Applied ethics propose a coherent set of principles to guide a critical reflection on the social and environmental consequences of integrative modelling and simulation of biodiversity scenarios. Such reflection should be incorporated into the actual modelling process, in a broad participatory framework, and foster effective moral involvement of modellers, policy-makers and stakeholders, in preference to the application of fixed ethical rules.

Keywords

Biodiversity Data Ethics Model Scenario Ethical principles 

Notes

Acknowledgments

We are grateful to three anonymous reviewers who have made a significant contribution to improving this article. This study was presented at the International Symposium “Investigating biodiversity and health at the human/animal/environment interface in the Nagoya Protocol era” held at the Faculty of Veterinary Technology, Kasetsart University, Bangkok (12–13 December 2017). It is a contribution to the ANR Project (2017-2021) No. ANR-17-CE35-0003-02 FutureHealthSEA “Predictive scenarios of health in Southeast Asia: linking land use and climate changes to infectious diseases”.

References

  1. Alcamo, Joseph. 2009. The SAS approach: combining qualitative and quantitative knowledge in environmental scenarios. In Environmental futures: The practice of environmental scenario analysis, ed. Joseph Alcamo, 123–150. Amsterdam: Elsevier.  https://doi.org/10.1016/S1574-101X(08)00406-7.Google Scholar
  2. Aletras, Nikolaos, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, and Vasileios Lampos. 2016. Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. PeerJ Computer Science 2: e93.  https://doi.org/10.7717/peerj-cs.93.Google Scholar
  3. Allenby, Braden R. 2014. Are new technologies undermining the laws of war? Bulletin of the Atomic Scientists 70 (1): 21–31.  https://doi.org/10.1177/0096340213516741.Google Scholar
  4. Article 29 DPWP (Data Protection Working Party). 2018. Guidelines on automated individual decision-making and profiling for the purposes of Regulation 2016/679 (as last revised and adopted on 6 February 2018). 17/EN WP251rev.01. ec.europa.eu/newsroom/article29/document.cfm?action=display&doc_id=49826.
  5. Barnosky, Anthony D., Nicholas Matzke, Susumu Tomiya, Guinevere O.U. Wogan, Brian Swartz, Tiago B. Quental, Charles Marshall, Jenny L. McGuire, Emily L. Lindsey, Kaitlin C. Maguire, Ben Mersey, and A. Ferrer Elizabeth. 2011. Has the Earth’s sixth mass extinction already arrived? Nature 471: 51–57.  https://doi.org/10.1038/nature09678.Google Scholar
  6. Beck, Marisa, and Tobias Krueger. 2016. The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modelling. WIREs Climate Change 7: 627–645.  https://doi.org/10.1002/wcc.415.Google Scholar
  7. Binder, Claudia R., Jochen Hinkel, Pieter W.G. Bots, and Pahl-Wostl Claudia. 2013. Comparison of frameworks for analyzing social-ecological systems. Ecology and Society 18 (4): 26.  https://doi.org/10.5751/ES-05551-180426.Google Scholar
  8. Boden, Lisa A., and Ian J. McKendrick. 2017. Model-based policymaking: a framework to promote ethical “good practice” in mathematical modelling for public health policymaking. Frontiers in Public Health 5: 68.  https://doi.org/10.3389/fpubh.2017.00068.Google Scholar
  9. Bolam, Friederike C., Matthew J. Grainger, Kerrie L. Mengersen, Gavin B. Stewart, William J. Sutherland, Michael C. Runge, Philip J. K. McGowan. 2018. Using the value of information to improve conservation decision making. Biological Reviews forthcoming.  https://doi.org/10.1111/brv.12471.
  10. Bostrom, Nick, and Eliezer Yudkowsky. 2014. The ethics of artificial intelligence. In The Cambridge handbook of artificial intelligence, ed. William M. Ramsay and Keith Frankish, 316–334. Cambridge: Cambridge University Press.Google Scholar
  11. Boyd, Dannah, and Kate Crawford. 2012. Critical questions for big data. Information, Communication & Society 15 (5): 662–679.  https://doi.org/10.1080/1369118X.2012.678878.Google Scholar
  12. Carlsson, Gunnar. 2009. Topology and data. Bulletin (new series) of the American Mathematical Society 46 (2): 255–308.  https://doi.org/10.1090/S0273-0979-09-01249-X.Google Scholar
  13. Casanovas, Pompeu, Louis De Koker, Danuta Mendelson, and David Watts. 2017. Regulation of big data: perspectives on strategy, policy, law and privacy. Health and Technology 7 (4): 335–349.  https://doi.org/10.1007/s12553-017-0190-6.Google Scholar
  14. CBD. 2011. Tkarihwaié:ri code of ethical conduct to ensure respect for the cultural and intellectual heritage of indigenous and local communities relevant to the conservation and sustainable use of biological diversity. Montreal: Secretariat of the Convention on Biological Diversity. https://www.cbd.int/doc/publications/ethicalconduct-brochure-en.pdf.
  15. Costa, Luiz. 2016. Virtuality and capabilities in a world of ambient intelligence. New challenges to privacy and data protection. Cham: Springer.  https://doi.org/10.1007/978-3-319-39198-4.Google Scholar
  16. Coveney, Peter V., Edward R. Dougherty, and Roger R. Highfield. 2016. Big data need big theory too. Philosophical Transactions of the Royal Society A 374: 20160153.  https://doi.org/10.1098/rsta.2016.0153.Google Scholar
  17. Danaher, John, Michael J. Hogan, Chris Noone, Rónán Kennedy, Anthony Behan, Aisling De Paor, Heike Felzmann, Muki Haklay, Su-Ming Khoo, John Morison, Maria Helen Murphy, Niall O'Brolchain, Burkhard Schafer, and Kalpana Shankar. 2017. Algorithmic governance: developing a research agenda through the power of collective intelligence. Big Data & Society 4 (2): 1–21.  https://doi.org/10.1177/2053951717726554.Google Scholar
  18. Ding, Helen, and Paolo A.L.D. Nunes. 2014. Modeling the links between biodiversity, ecosystem services and human wellbeing in the context of climate change: results from an econometric analysis of the European forest ecosystems. Ecological Economics 90: 60–73.  https://doi.org/10.1016/j.ecolecon.2013.11.004.Google Scholar
  19. Dugarova, Esuna, and Nergis Gülasan. 2017. Global trends: challenges and opportunities in the implementation of the Sustainable Development Goals. Geneva: United Nations Development Programme and United Nations Research Institute for Social Development.Google Scholar
  20. Edmonds, Bruce, and David Hales. 2003. Replication, replication and replication: some hard lessons from model alignment. Journal of Artificial Societies and Social Simulation 6 (4): 1–11 http://jasss.soc.surrey.ac.uk/6/4/11.html.Google Scholar
  21. Friedman, Batya, Peter H. Kahn Jr., and Alan Borning. 2008. Value sensitive design and information systems. In The handbook of information and computer ethics, ed. Kenneth Einar Himma and Herman T. Tavani, 69–101. Hoboken: Wiley.Google Scholar
  22. Funtowicz, Silvio O., and Jerome R. Ravetz. 1993. Science for the post-normal age. Futures 25 (7): 739–755.  https://doi.org/10.1016/0016-3287(93)90022-L.Google Scholar
  23. Gaudou, Benoit, Christophe Sibertin-Blanc, Olivier Therond, Frédéric Amblard, Yves Auda, Jean-Paul Arcangeli, Maud Balestrat, Marie-Hélène Charron-Moirez, Etienne Gondet, Hong Yi, Romain Lardy, Thomas Louail, Eunate Mayor, David Panzoli, Sabine Sauvage, José-Miguel Sánchez-Pérez, Patrick Taillandier, Nguyen Van Bai, Maroussia Vavasseur, and Pierre Mazzega. 2014. The MAELIA multi-agent platform for integrated analysis of interactions between agricultural land-use and low-water management strategies. In Multi-agent-based simulation XIV: International Workshop, MABS 2013, Saint Paul, MN, USA, May 6–7, 2013, Revised Selected Papers, ed. Shah Jamal Alam and H. van Dyke Parunak, 85–100. Heidelberg: Springer.Google Scholar
  24. GEO BON (Group on Earth Observations, Biodiversity Observation Network). 2015. Global biodiversity change indicators, version 1.2. Leipzig: Group on Earth Observations Biodiversity Observation Network Secretariat http://orbit.dtu.dk/files/118107008/GBCI_Version1.2_low_Biodiversity_Index.pdf.Google Scholar
  25. Gotterbarn, Donald, Keith Miller, and Simon Rogerson. 1999. Computer society and ACM approve software engineering code of ethics. Computer Society Connection 32 (10): 84–88.Google Scholar
  26. Grüber, Thomas R. 1995. Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies 43 (5–6): 907–928.Google Scholar
  27. Hammond, Allen L., Albert Adriaanse, Eric Rodenburg, Dirk Bryant, and Richard Woodward. 1995. Environmental indicators: a systematic approach to measuring and reporting on environmental policy performance in the context of sustainable development. Washington, DC: World Resources Institute.Google Scholar
  28. Hansen, Hans Krause, and Tony Porter. 2017. What do big data do in global governance? Global Governance 23 (1): 31–42.  https://doi.org/10.5555/1075-2846.23.1.31.Google Scholar
  29. Harfoot, Michael B.J., Tim Newbold, Derek P. Tittensor, Stephen Emmott, Jon Hutton, Vassily Lyutsarev, Matthew J. Smith, Jörn P.W. Scharlemann, and W. Purves Drew. 2014. Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model. PLoS Biology 12 (4): e1001841.  https://doi.org/10.1371/journal.pbio.1001841.Google Scholar
  30. Head, Brian W., and John Alford. 2013. Wicked problems: implications for public policy and management. Administration and Society 47 (6): 711–739  https://doi.org/10.1177/0095399713481601.Google Scholar
  31. ISSC and UNESCO. 2013. World social science report 2013: changing global environments. Paris: OECD Publishing and UNESCO Publishing http://unesdoc.unesco.org/images/0022/002246/224677e.pdf.Google Scholar
  32. Jakeman, Anthony J., Rebecca A. Letcher, and John P. Norton. 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21 (5): 602–614.  https://doi.org/10.1016/j.envsoft.2006.01.004.Google Scholar
  33. Lajaunie, Claire, Serge Morand, and Pierre Mazzega. 2018. Complexity of scenarios of future health: integrating policies and Laws. In Law, public policies and complex systems: networks in action, ed. R. Boulet, Claire Lajaunie, and Pierre Mazzega. Berlin: Springer forthcoming.Google Scholar
  34. Lazarus, Richard J. 2009. Super wicked problems and climate change: restraining the present to liberate the future. Cornell Law Review 94 (5): 1153–1233 http://scholarship.law.cornell.edu/clr/vol94/iss5/8.Google Scholar
  35. Leadley, Paul, Henrique Miguel Pereira, Rob Alkemade, Juan F. Fernandez-Manjarrés, Vânia Proença, Jörn P.W. Scharlemann, and Matt J. Walpole. 2010. Biodiversity scenarios: projections of 21 century change in biodiversity and associated ecosystem services. A Technical Report for the Global Biodiversity Outlook 3. CBD Technical Series no. 50. Montreal: Secretariat of the Convention on Biological Diversity.Google Scholar
  36. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature 521: 436–444.  https://doi.org/10.1038/nature14539.Google Scholar
  37. Levine, Simon A. 1992. The problem of pattern and scale in ecology. Ecology 73 (6): 1943–1967.  https://doi.org/10.2307/1941447.Google Scholar
  38. Lowrie, Ian. 2017. Algorithmic rationality: Epistemology and efficiency in the data sciences. Big Data & Society 2017: 1–13.  https://doi.org/10.1177/2053951717700925.Google Scholar
  39. Mazzega, Pierre, Claire Lajaunie, and Ettiene Fieux. 2018. Governance modelling: dimensionality and conjugacy. In Graph Theory: Advanced Algorithms and Applications, ed. Beril Sirmacek, 63–82. Rijeka: IntechOpen.  https://doi.org/10.5772/intechopen.71774.Google Scholar
  40. Mazzega, Pierre, Christophe Sibertin-Blanc and Olivier Therond. 2016. Consideration of decision-making processes in agent-based models of social-ecological systems. In 8 International Congress on Environmental Modelling and Software - Toulouse, France - July 2016, ed. S. Sauvage, J.-M. Sánchez-Pérez and A. Rizzoli. Available on: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=1360&context=iemssconference. Accessed 21 March 2018.
  41. Mazzega, Pierre, Olivier Therond, Thomas Debril, Hug March, Christophe Sibertin-Blanc, Romain Lardy, and Daniel R. Sant’Ana. 2014. Critical multi-scale governance issues of the integrated modelling: example of the lowwater management in the Adour-Garonne basin (France). Journal of Hydrology 519: 2515–2526.  https://doi.org/10.1016/j.jhydrol.2014.09.043.Google Scholar
  42. Morand, Serge and Claire Lajaunie. 2018. Biodiversity and health. Linking life, ecosystems and societies. London: ISTE Press / Oxford: Elsevier.Google Scholar
  43. Ören, Tuncer I. 2000. Responsibility, ethics, and simulation. Transactions of The Society for Computer Simulation International 17 (4): 165–170.Google Scholar
  44. Ören, Tuncer I., Maurice S. Elzas, Iva Smit and Louis G. Birta. 2002. A code of professional ethics for simulationists. Proceedings of the 2002 Summer Computer Simulation Conference, 434-435. San Diego, CA, 13-18 July 2002.Google Scholar
  45. Ostrom, Elinor. 2009. A general framework for analyzing sustainability of social-ecological systems. Science 325 (5939): 419–422.  https://doi.org/10.1126/science.1172133.Google Scholar
  46. Ostrom, Elinor. 2010. Institutional analysis and development: elements of the framework in historical perspective. In Encyclopedia of life support systems, volume 2 of historical developments and theoretical approaches in sociology, edited by Charles Crothers, 261–288. EOLSS Publications.Google Scholar
  47. Palmer, Erika. 2017. Beyond proximity: consequentialist ethics and system dynamics. Etikk i praksis: Nordic Journal of Applied Ethics 11 (1): 89–105.  https://doi.org/10.5324/eip.v11i1.1978.Google Scholar
  48. Pascual, Mercedes, and Simon A. Levin. 1999. From individuals to population densities: searching for the intermediate scale of nontrivial determinism. Ecology 80 (7): 2225–2236. https://doi.org/10.1890/0012-9658(1999)080[2225:FITPDS]2.0.CO;2.Google Scholar
  49. Pereira, Henrique M., Paul W. Leadley, Vânia Proença, Rob Alkemade, Jörn P.W. Scharlemann, Juan F. Fernandez-Manjarrés, Miguel B. Araújo, Patricia Balvanera, Reinette Biggs, William W.L. Cheung, Louise Chini, H. David Cooper, Eric L. Gilman, Sylvie Guénette, George C. Hurtt, Henry P. Huntington, Georgina M. Mace, Thierry Oberdorff, Carmen Revenga, Patrícia Rodrigues, Robert J. Scholes, Ussif Rashid Sumaila, and Walpole Matt. 2010. Scenarios for global biodiversity in the 21 century. Science 330: 1496–1501.  https://doi.org/10.1126/science.1196624.Google Scholar
  50. Primmer, Eeva, Mette Termansen, Yennie Bredin, Malgorzata Blicharska, García‐Llorente Marina, Pam Berry, Tiina Jääskeläinen, Györgyi Bela, Veronika Fabok, Nicoleta Geamana, Paula A. Harrison, John R. Haslett, Georgia Lavinia Cosor, and H.K. Andersen Anne. 2017. Caught between personal and collective values: biodiversity conservation in European decision-making. Environmental Policy and Governance 27 (6): 588–604.  https://doi.org/10.1002/eet.1763.Google Scholar
  51. Pryshlakivsky, Jonathan, and Cory Searcy. 2013. Sustainable development as a wicked problem. In Managing and engineering in complex situations, ed. Samuel F. Kovacic and Andres Sousa-Poza, 109–128. Heidelberg: Springer.  https://doi.org/10.1007/978-94-007-5515-4_6.Google Scholar
  52. Remagnino, Paolo, and Gian Luca Foresti. 2005. Ambient intelligence: a new multidisciplinary paradigm. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 35 (1): 1–6.  https://doi.org/10.1109/TSMCA.2004.838456.Google Scholar
  53. Ripple, William J., Christopher Wolf, Thomas M. Newsome, Mauro Galetti, Mohammed Alamgir, Eileen Crist, Mahmoud I. Mahmoud, William F. Laurance, and 15,364 scientist signatories from 184 countries. 2017. World scientists’ warning to humanity: a second notice. BioScience 67 (12): 1026–1028.  https://doi.org/10.1093/biosci/bix125.Google Scholar
  54. Risbey, James, Milind Kandlikar, and Anand Patwardhan. 1996. Assessing integrated assessments. Climatic Change 34: 369–395.  https://doi.org/10.1007/BF00139298.Google Scholar
  55. Rittel, Horst W.J., and Melvin M. Webber. 1973. Dilemmas in a general theory of planning. Policy Sciences 4 (2): 155–169.  https://doi.org/10.1007/BF01405730.Google Scholar
  56. Ruppert, Evelyn, Engin Isin, and Didier Bigo. 2017. Data politics. Big Data & Society 2017: 1–7.  https://doi.org/10.1177/2053951717717749.Google Scholar
  57. Schmidhuber, Jürgen. 2015. Deep learning in neural networks: an overview. Neural Networks 61: 85–117.  https://doi.org/10.1016/j.neunet.2014.09.003.Google Scholar
  58. Schwartz, Arthur E. 2017. Engineering society codes of ethics. A bird’s-eye view. The Bridge 47 (1): 21–26.Google Scholar
  59. Seaver, Nick. 2017. Algorithms as culture: some tactics for the ethnography of algorithmic systems. Big Data & Society 2017: 1–12.  https://doi.org/10.1177/2053951717738104.Google Scholar
  60. Sharman, Martin, and Musa C. Mlambo. 2012. Wicked: the problem of biodiversity loss. GAIA 21 (4): 274–277.  https://doi.org/10.14512/gaia.21.4.10.Google Scholar
  61. Sibertin-Blanc, Christophe, Olivier Therond, Claude Monteil, and Pierre Mazzega. 2018. The entity-process framework for integrated agent-based modelling of social-ecological systems. In Law, public policies and complex systems: networks in action, ed. R. Boulet, Claire Lajaunie, and Pierre Mazzega. Berlin: Springer forthcoming.Google Scholar
  62. Soranno, Patricia A., Kendra Spence Cheruvelil, Kevin C. Elliott, and Georgina M. Montgomery. 2015. It’s good to share: why environmental scientists’ ethics are out of date. BioScience 65 (1): 69–73.  https://doi.org/10.1093/biosci/biu169.Google Scholar
  63. Sun, Jiazhe, and Kaizhong Yang. 2016. The wicked problem of climate change: a new approach based on social mess and fragmentation. Sustainability 8 (12): 1312.  https://doi.org/10.3390/su8121312.Google Scholar
  64. Surminski, Swenja, and Andrew Williamson. 2014. Policy indexes as tools for decision makers: the case of climate policy. Global Policy 5 (3): 275–285.  https://doi.org/10.1111/1758-5899.12121.Google Scholar
  65. Tuana, Nancy. 2010. Leading with ethics, aiming for policy: new opportunities for philosophy of science. Synthese 177 (3): 471–492.  https://doi.org/10.1007/s11229-010-9793-4.Google Scholar
  66. UNSG-IEAG (United Nations Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development). 2014. A world that counts: mobilising the data revolution for sustainable development. New York: Independent Expert Advisory Group Secretariat http://www.objetivosdedesarrollodelmilenio.org.mx/Doctos/UnMunCta_ing.pdf.Google Scholar
  67. van Delden, Hedwig, Ralf Seppelt, Roger White, and Anthony J. Jakeman. 2011. A methodology for the design and development of integrated models for policy support. Environmental Modelling and Software 26 (3): 266–279.  https://doi.org/10.1016/j.envsoft.2010.03.021.Google Scholar
  68. van den Hoven, Jeroen. 2008. Moral methodology and information technology. In The handbook of information and computer ethics, ed. Kenneth Einar Himma and Herman T. Tavani, 49–67. Hoboken: Wiley.Google Scholar
  69. van de Voort, Marlies, Wolter Pieters, and Luca Consoli. 2015. Refining the ethics of computer-made decisions: a classification of moral mediation by ubiquitous machines. Ethics and Information Technology 17 (1): 41–56.  https://doi.org/10.1007/s10676-015-9360-2.Google Scholar
  70. Zundert, van, Smiljana Antonijevic Joris, Anne Beaulieu, Karina van Dalen-Oskam, Douwe Zeldenrust, and Tara L. Andrews. 2012. Cultures of formalisation: towards an encounter between humanities and computing. In Understanding Digital Humanities, ed. D.M. Berry, 279–294. Basingstoke: Palgrave Macmillan.  https://doi.org/10.1057/9780230371934_15.Google Scholar
  71. WFEO/FMOI-UNESCO (World Federation of Engineering Organizations). 2001. WFEO Model code of ethics. https://www.sustainable-design.ie/fire/WFEO-UNESCO_Model-Code-Ethics_2001.pdf
  72. Wise, Alyssa Friend, and W. Shaffer David. 2015. Why theory matters more than ever in the age of big data. Journal of Learning Analytic 2 (2): 5–13.  https://doi.org/10.18608/jla.2015.22.2.Google Scholar
  73. Wynne, Brian. 1992. Uncertainty and environmental learning. Reconceiving science and policy in the preventive paradigm. Global Environmental Change 2 (2): 111–127.  https://doi.org/10.1016/0959-3780(92)90017-2.Google Scholar

Copyright information

© National University of Singapore and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.UMR5563 GET Geosciences Environment ToulouseCNRS / University of ToulouseToulouseFrance
  2. 2.Affiliate Researcher, Strathclyde Center for Environmental Law and GovernanceUniversity of StrathclydeGlasgowUK

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