{"id":100,"date":"2021-04-12T01:34:58","date_gmt":"2021-04-12T05:34:58","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/?post_type=chapter&p=100"},"modified":"2021-07-05T01:06:13","modified_gmt":"2021-07-05T05:06:13","slug":"overview-3","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/chapter\/overview-3\/","title":{"raw":"Overview","rendered":"Overview"},"content":{"raw":"Throughout this course, we have been (explicitly or not) incorporating probabilistic methods to formulate conjectures about future states of the world. That approach is typically considered appropriate when exact quantities or values cannot be reliably determined, but the range and distribution of values can. Recall, for example, that in Module 1 we used a hazard distribution function as the basis for our simulations regarding the frequency and extent of exposure in our catastrophe models. Incorporating probability distributions in this way enables us to incorporate uncertainty into our predictions, and to explore (and prepare for) potential variability in outcomes.\r\n\r\nBut what happens when those probability distributions are not fully understood? Or are wrong? What happens if they change over time, or reach a point they no longer accurately describe reality?\r\n\r\nWe can think of those possibilities as deep uncertainty: situations in which adequate probabilistic information, or agreement about the correct<\/i> probabilities, is lacking. Deep uncertainty is unsettling. It limits our capacity to make predictions about the future, which therefore undermines our capacity to make good decisions to meet that future.\r\n\r\nIn this module, after exploring two examples of how our probabilistic method could potentially let us down, we\u2019ll look at decision-making models that are less reliant on probability distributions, but that can produce meaningful and adaptive strategies even amidst deep uncertainty.\r\n
\r\n

Module Objectives<\/p>\r\n\r\n<\/header>\r\n

\r\n\r\nBy the end of this unit, you will:\r\n
    \r\n \t
  1. Understand the uncertainty arising from the complex dynamic nature of climate systems, socio-political systems, and the inter-relationships between them;<\/li>\r\n \t
  2. Define the \"tail risks\" in hazard distributions and understand the implications of \"fat tails\" on adaptation decision-making;<\/li>\r\n \t
  3. Identify examples of models for decision-making under deep uncertainty (adaptation pathways, real options, robust decision making);<\/li>\r\n \t
  4. Contemplate the relevance of the precautionary principle<\/i> when the probability of catastrophic outcomes are greater than zero.<\/li>\r\n<\/ol>\r\n<\/div>\r\n<\/div>\r\n \r\n
    \r\n

    Readings & Resources<\/p>\r\n\r\n<\/header>\r\n

    \r\n\r\nMcSweeney, R. (2020).\u00a0Explainer: Nine \u2018tipping points\u2019 that could be triggered by climate change<\/a>.<\/i>\u00a0Carbon Brief.\r\n\r\nvan Ginkel, K.C.H., Wouter Botzen, W.J., Haasnoot, M., Bachner, G., Steininger, K.W., Hinkel, J., Watkiss, P., Boere, E., Jeuken, A., de Murieta, E.S., & Bosello, F. (2020).\u00a0Climate change induced socio-economic tipping points: Review and stakeholder consultation for policy relevant research<\/a>. Environ. Res. Lett. 15<\/i>.\r\n\r\nWatkiss, P., Hunt, A., Blyth, W., & Dyszynski, J. (2015). The use of new economic decision support tools for adaptation assessment: A review of methods and applications, towards guidance on applicability. Climatic Change, 132<\/i>(3). <\/i>[Excerpts only].<\/b>\u00a0This article was made pursuant to the Fair Dealing Policy of the University.\u00a0The article may only be used for the purpose of research, private study, criticism, review, news reporting, education, satire or parody. The use of this copy for any other purpose may require the permission of the copyright owner.<\/i>\r\n\r\n<\/div>\r\n<\/div>\r\n ","rendered":"

    Throughout this course, we have been (explicitly or not) incorporating probabilistic methods to formulate conjectures about future states of the world. That approach is typically considered appropriate when exact quantities or values cannot be reliably determined, but the range and distribution of values can. Recall, for example, that in Module 1 we used a hazard distribution function as the basis for our simulations regarding the frequency and extent of exposure in our catastrophe models. Incorporating probability distributions in this way enables us to incorporate uncertainty into our predictions, and to explore (and prepare for) potential variability in outcomes.<\/p>\n

    But what happens when those probability distributions are not fully understood? Or are wrong? What happens if they change over time, or reach a point they no longer accurately describe reality?<\/p>\n

    We can think of those possibilities as deep uncertainty: situations in which adequate probabilistic information, or agreement about the correct<\/i> probabilities, is lacking. Deep uncertainty is unsettling. It limits our capacity to make predictions about the future, which therefore undermines our capacity to make good decisions to meet that future.<\/p>\n

    In this module, after exploring two examples of how our probabilistic method could potentially let us down, we\u2019ll look at decision-making models that are less reliant on probability distributions, but that can produce meaningful and adaptive strategies even amidst deep uncertainty.<\/p>\n

    \n
    \n

    Module Objectives<\/p>\n<\/header>\n

    \n

    By the end of this unit, you will:<\/p>\n

      \n
    1. Understand the uncertainty arising from the complex dynamic nature of climate systems, socio-political systems, and the inter-relationships between them;<\/li>\n
    2. Define the “tail risks” in hazard distributions and understand the implications of “fat tails” on adaptation decision-making;<\/li>\n
    3. Identify examples of models for decision-making under deep uncertainty (adaptation pathways, real options, robust decision making);<\/li>\n
    4. Contemplate the relevance of the precautionary principle<\/i> when the probability of catastrophic outcomes are greater than zero.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n

       <\/p>\n

      \n
      \n

      Readings & Resources<\/p>\n<\/header>\n

      \n

      McSweeney, R. (2020).\u00a0Explainer: Nine \u2018tipping points\u2019 that could be triggered by climate change<\/a>.<\/i>\u00a0Carbon Brief.<\/p>\n

      van Ginkel, K.C.H., Wouter Botzen, W.J., Haasnoot, M., Bachner, G., Steininger, K.W., Hinkel, J., Watkiss, P., Boere, E., Jeuken, A., de Murieta, E.S., & Bosello, F. (2020).\u00a0Climate change induced socio-economic tipping points: Review and stakeholder consultation for policy relevant research<\/a>. Environ. Res. Lett. 15<\/i>.<\/p>\n

      Watkiss, P., Hunt, A., Blyth, W., & Dyszynski, J. (2015). The use of new economic decision support tools for adaptation assessment: A review of methods and applications, towards guidance on applicability. Climatic Change, 132<\/i>(3). <\/i>[Excerpts only].<\/b>\u00a0This article was made pursuant to the Fair Dealing Policy of the University.\u00a0The article may only be used for the purpose of research, private study, criticism, review, news reporting, education, satire or parody. The use of this copy for any other purpose may require the permission of the copyright owner.<\/i><\/p>\n<\/div>\n<\/div>\n

       <\/p>\n","protected":false},"author":120,"menu_order":1,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[48],"contributor":[],"license":[],"class_list":["post-100","chapter","type-chapter","status-publish","hentry","chapter-type-numberless"],"part":96,"_links":{"self":[{"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/chapters\/100","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/wp\/v2\/users\/120"}],"version-history":[{"count":6,"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/chapters\/100\/revisions"}],"predecessor-version":[{"id":273,"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/chapters\/100\/revisions\/273"}],"part":[{"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/parts\/96"}],"metadata":[{"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/chapters\/100\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/wp\/v2\/media?parent=100"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/pressbooks\/v2\/chapter-type?post=100"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/wp\/v2\/contributor?post=100"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/financialimpactclimatechange\/wp-json\/wp\/v2\/license?post=100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}