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class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://oxford.academia.edu/AlexanderMorrison">Alexander Morrison</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">University of Oxford</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a data-nosnippet="" href="https://dal.academia.edu/GregoryHanlon"><img class="profile-avatar u-positionAbsolute" alt="Gregory Hanlon related author profile picture" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/76139/21058/150660415/s200_gregory.hanlon.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://dal.academia.edu/GregoryHanlon">Gregory Hanlon</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Dalhousie University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a data-nosnippet="" href="https://gc-cuny.academia.edu/ClaireBishop"><img class="profile-avatar u-positionAbsolute" alt="Claire Bishop related author profile picture" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/102167/28125/113712184/s200_claire.bishop.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://gc-cuny.academia.edu/ClaireBishop">Claire Bishop</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Graduate Center of the City University of New York</p></div></div><div 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href="https://29mayis.academia.edu/esg"><img class="profile-avatar u-positionAbsolute" alt="Emrah Safa Gürkan (ESG) related author profile picture" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/140012/37138/31045044/s200_emrah_safa.g_rkan_esg_.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://29mayis.academia.edu/esg">Emrah Safa Gürkan (ESG)</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Istanbul 29 Mayis University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a data-nosnippet="" href="https://upenn.academia.edu/RenataHolod"><img class="profile-avatar u-positionAbsolute" alt="Renata Holod related author profile picture" 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class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://qc-cuny.academia.edu/WarrenWoodfin">Warren Woodfin</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Queens College of the City University of New York</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a data-nosnippet="" href="https://vanderbilt.academia.edu/jelenabogdanovic"><img class="profile-avatar u-positionAbsolute" alt="jelena bogdanovic related author profile picture" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/952546/915546/1144207/s200_jelena.bogdanovic.jpg" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://vanderbilt.academia.edu/jelenabogdanovic">jelena bogdanovic</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Vanderbilt University</p></div></div></ul></div><style type="text/css">.suggested-academics--header h3{font-size:16px;font-weight:500;line-height:20px}</style><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="4219641" href="https://www.academia.edu/Documents/in/Art_History"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/StevenPopper","location":"/StevenPopper","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/StevenPopper","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" 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class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Steven Popper</h3></div><div class="js-work-strip profile--work_container" data-work-id="127404624"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/127404624/Confronting_Model_Uncertainty"><img alt="Research paper thumbnail of Confronting Model Uncertainty" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title">Confronting Model Uncertainty</div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="127404624"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="127404624"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 127404624; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=127404624]").text(description); $(".js-view-count[data-work-id=127404624]").attr('title', 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class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/61443783/Reflections_DMDU_and_Public_Policy_for_Uncertain_Times">Reflections: DMDU and Public Policy for Uncertain Times</a></div><div class="wp-workCard_item"><span>Decision Making under Deep Uncertainty</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Public policy has always confronted future uncertainties. Projecting likely futures has been view...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Public policy has always confronted future uncertainties. Projecting likely futures has been viewed as best practice for assessing proposed plans even though few would expect exactly those futures to occur. But in an era of deep uncertainties in which prior rules of thumb are no longer believed likely to hold true in years to come, sufficient diligence for policy analysis demands a different standard. • DMDU approaches collectively represent an evolving capacity to deal with the challenge of the future by providing a technology of complexity, especially in the analysis of problems in public policy. • New approaches to policy analysis may provide the means to enable policy processes better suited to deep uncertainty and dynamic change. And the recognition by policymakers that there exist (and that they should demand) new means for analysis that comport better to the emerging needs of policy would, in turn, allow more rapid diffusion of technique into realms not yet exposed to means for decisionmaking under deep uncertainty. • The interaction between analysts and policymakers requires contact between two distinct cultures. DMDU applications collectively present a body of theory and practice with the potential for providing a common vocabulary to the work of both analysts and of those charged with policy design and implementation during uncertain times. • For a problem requiring treatment by DMDU approaches, "any job worth doing is worth doing superficially." An analysis based on an initial fast and simple exploratory model will frequently elucidate many of the major interactions between policy choices and the problem system. (Exploratory Modeling is discussed extensively in Chaps. 2, 7, and 15.) Those factors that appear most salient may then be examined in more detailed fashion in later model revisions and recalibrations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cba01d6fc01c040225784dc72156c533" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74472093,"asset_id":61443783,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74472093/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61443783"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61443783"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61443783; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61443783]").text(description); $(".js-view-count[data-work-id=61443783]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61443783; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61443783']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cba01d6fc01c040225784dc72156c533" } } $('.js-work-strip[data-work-id=61443783]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61443783,"title":"Reflections: DMDU and Public Policy for Uncertain Times","translated_title":"","metadata":{"publisher":"Springer International Publishing","ai_title_tag":"DMDU Strategies for Public Policy Uncertainty","grobid_abstract":"Public policy has always confronted future uncertainties. Projecting likely futures has been viewed as best practice for assessing proposed plans even though few would expect exactly those futures to occur. But in an era of deep uncertainties in which prior rules of thumb are no longer believed likely to hold true in years to come, sufficient diligence for policy analysis demands a different standard. • DMDU approaches collectively represent an evolving capacity to deal with the challenge of the future by providing a technology of complexity, especially in the analysis of problems in public policy. • New approaches to policy analysis may provide the means to enable policy processes better suited to deep uncertainty and dynamic change. 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Projecting likely futures has been viewed as best practice for assessing proposed plans even though few would expect exactly those futures to occur. But in an era of deep uncertainties in which prior rules of thumb are no longer believed likely to hold true in years to come, sufficient diligence for policy analysis demands a different standard. • DMDU approaches collectively represent an evolving capacity to deal with the challenge of the future by providing a technology of complexity, especially in the analysis of problems in public policy. • New approaches to policy analysis may provide the means to enable policy processes better suited to deep uncertainty and dynamic change. And the recognition by policymakers that there exist (and that they should demand) new means for analysis that comport better to the emerging needs of policy would, in turn, allow more rapid diffusion of technique into realms not yet exposed to means for decisionmaking under deep uncertainty. • The interaction between analysts and policymakers requires contact between two distinct cultures. DMDU applications collectively present a body of theory and practice with the potential for providing a common vocabulary to the work of both analysts and of those charged with policy design and implementation during uncertain times. • For a problem requiring treatment by DMDU approaches, \"any job worth doing is worth doing superficially.\" An analysis based on an initial fast and simple exploratory model will frequently elucidate many of the major interactions between policy choices and the problem system. (Exploratory Modeling is discussed extensively in Chaps. 2, 7, and 15.) 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This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit <a href="http://www.rand.org/pubs/permissions" rel="nofollow">www.rand.org/pubs/permissions</a>. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-61443772-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-61443772-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193099/figure-2-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193091/figure-1-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193112/figure-3-source-national-highway-transportation-safety"><img alt="SOURCE: National Highway Transportation Safety Administration, no date. RAND RR2333-S.1 Decades-Long Progress in Reducing Roadway Deaths Has Begun Reversing in the Past Five Years We Know Who Is Dying and Why " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193117/figure-4-sources-older-teen-young-adult-crashes-centers-for"><img alt="SOURCES: Older teen/young adult crashes: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, no date (b); Men: Insurance Institute for Highway Safety, 2017a; People in rural areas: NHTSA, 2017b; Population: Howden and Meyer, 2011. NOTE: Crash data for 2015; proportion of population from 2010 Census. RAND RR2333-S.2 Proportion of U.S. Roadway Deaths Relative to Proportion of Population " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193122/figure-5-these-three-approaches-are-essential-and"><img alt="‘These three approaches are essential and interconnected; none of the three will work effectively independent of the others. They are complementary, mutually dependent, and syn- ergistic. (See Figure S.3.) For example, a growing safety culture will foster safe behaviors, such as driving sober and within the speed limit, and create a strong market for advanced safety technologies (including automated vehicles). As people become accustomed to the safety benefits of advanced technology and improved roads, they will become less tolerant of risky behavior and more supportive of the changes needed to build a Safe System. ‘The effect of each change is intertwined with the others and mutually supportive—a “virtuous cycle.” " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193126/figure-6-sources-nhtsa-data-nhtsa-no-date-fars-data-census"><img alt="SOURCES: NHTSA, 2017c (2016 data); NHTSA, no date (FARS 2011 data); U.S. Census Bureau, 2016; Federal Highway Administration, 2016. RAND RR2333-1.7 Increases in Deaths by Road User Type, 2011 to 2016 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193129/figure-7-sources-older-teen-young-adult-crashes-cdc-national"><img alt="SOURCES: Older teen/young adult crashes: CDC, National Center for Injury Prevention and Control, no date (b); Men: IIHS, 2017a; People in rural areas: NHTSA, 2017b; Population: Howden and Meyer, 2011. NOTE: Crash data for 2015; proportion of population from 2010 Census. RAND RR2333-1.2 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193139/figure-8-sources-adapted-from-seppa-and-reason-rand-rr"><img alt="SOURCES: Adapted from Seppa (2013) and Reason (2000). RAND RR2333-1.3 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_008.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193144/figure-9-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_009.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193148/figure-10-source-adapted-from-curry-and-hodgson-rand-rr"><img alt="SOURCE: Adapted from Curry and Hodgson, 2008. RAND RR2333-A.1 Three-Horizon Foresight Provides a Framework for Visualizing Change over Time " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_010.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193149/figure-11-assumption-based-planning-helps-groups-find-hidden"><img alt="Assumption-Based Planning Helps Groups Find Hidden Assumptions and Weigh Risks 3 A more accurate—but vastly more confusing—representation of the curves associated with the First and Second Hori- zons would be a myriad of curves all slightly displaced, so that, over time, we could see supporting systems evolving with changing conditions and demands. These might be portrayed as being of different heights (that is, differing degrees of fit ness) at different points along the time line if changes in internal and external conditions overwhelm our ability to changs policies, institutions, and other structures swiftly enough to accommodate those changes. Figure A.2 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_011.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193152/figure-12-combining-hf-with-abp-weighs-both-future-visions"><img alt="Combining 3HF with ABP Weighs Both Future Visions and Alternative Strategic Pathways " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_012.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193160/table-1-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193162/table-2-sources-nhtsa-census-bureau-federal-highway"><img alt="SOURCES: NHTSA, 2017c; U.S. Census Bureau, 2016; Federal Highway Administration, 2016. Three Indicators of Roadway Deaths Have Worsened Since 2011 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193163/table-3-participants-in-the-three-worksops-the-people-listed"><img alt="Participants in the Three Worksops The people listed below participated in one or more of the three workshops. " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193165/table-4-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193166/table-5-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_005.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-61443772-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b32af5dc09a118ea8787dc870f6f4489" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74472125,"asset_id":61443772,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74472125/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61443772"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61443772"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61443772; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61443772]").text(description); $(".js-view-count[data-work-id=61443772]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61443772; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61443772']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b32af5dc09a118ea8787dc870f6f4489" } } $('.js-work-strip[data-work-id=61443772]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61443772,"title":"The Road to Zero: A Vision for Achieving Zero Roadway Deaths by 2050","translated_title":"","metadata":{"publisher":"RAND Corporation","ai_title_tag":"A Vision for Achieving Zero Roadway Deaths by 2050","grobid_abstract":"Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. 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RDM was intended for use with formal models. However, we show a model-less RDM application to a portfolio planning problem (selecting U.S. Army security cooperation activities with a partner country) seeking to achieve several objectives. In the absence of formal models, the analysis tests candidate actions against different explicit statements of causal relationships to allow more systematic reasoning over choices and outcomes. Doing so renders assumptions about complex systems explicit, characterizes uncertainties in terms of effect on weighting policy choices rather than as presently unknowable probabilities, provides a venue for planners and evaluators to share findings and insights, and yields explicit expressions of theories of causation (TOC) that themselves act as formal models where none had previously existed. RDM allows important scenarios for decision planning to be generated analytically. This may address for several scenario applications the question of determining important futures and also how scenario results may be used directly to inform policy and operational decisions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="38453f9cc3ce6b0871a39ac01ec37c47" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74472127,"asset_id":61443771,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74472127/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61443771"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61443771"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61443771; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61443771]").text(description); $(".js-view-count[data-work-id=61443771]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61443771; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61443771']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "38453f9cc3ce6b0871a39ac01ec37c47" } } $('.js-work-strip[data-work-id=61443771]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61443771,"title":"Robust decision making and scenario discovery in the absence of formal models","translated_title":"","metadata":{"publisher":"Wiley","ai_title_tag":"Model-less Robust Decision Making in Planning","grobid_abstract":"Robust decision making (RDM) is a method for aiding decision making under deep uncertainty that uses models not as predictors but as generators of cases exploring assumptions and outcomes. 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This electronic representation of RAND intellectual property is provided for non-commercial use only. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-61443768-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-61443768-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090064/figure-1-of-the-desired-educational-outcomes-am-making-no"><img alt="of the desired educational outcomes (I am making no assumption that these can be adequately measured with standardized exams). Everyone’s actual educational goal is some preference over the distribution of completed learning—which could be that everyone be above some threshold or that the average be high, for example. Suppose we had an MLG based on a goal for minimally adequate learning (Filmer, Hasan, and Pritchett, 2006). One could then assume some cause-and-effect relationship between a measure of time spent in school (or grade pro- gression) on the horizontal axis and progress on the educational goal on the vertical axis and assume that the curricular design were such that completing the one (primary schooling, say) led to the other. Of course, assumptions can prove wrong, and there are three huge problems with connection between an MDG and an MLG. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090074/figure-2-disconnect-does-curricular-design-lead-to-the-mlg"><img alt="Disconnect 1. Does curricular design lead to the MLG at the MDG? " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090080/figure-3-distribution-of-test-scores-in-the-pisa-for"><img alt="Distribution of Test Scores in the 2003 PISA for Mathematics for Three Countries " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090081/figure-4-changes-in-full-time-wages-by-education-level-the"><img alt="Changes in U.S. Full-Time Wages, by Education Level, 1981-2005 ‘The third issue around the top end is globalization. One possible explanation is that returns to superstars increased because globalization allowed the highly skilled to leverage their talents over larger and larger markets. If that is the case, what this implies for the returns to a country’s economic elite that is globally low skilled is not clear. If they compete head to head (so to speak), they lower their wages or returns. This also may distort their relative returns so that they allocate into nontradable activities (e.g., government, law, medicine) and away from activities in which their skills are pitted head to head against global talent. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090082/figure-5-source-autor-katz-and-kearney-using-march-current"><img alt="SOURCE: Autor, Katz, and Kearney (2005), using March Current Population Survey (CPS) data, 1976-2004. NOTE: Statistics pool three years of data. College graduates are those with 16 or 17 years of completed schooling (surveys prior to 1992) or a baccalaureate degree only (1992 forward). RAND CF267-3.4 " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090083/figure-6-the-virtue-of-success-in-socialization-is-that"><img alt="the virtue of success in socialization is that complete success means the complete invisibility of inevitability: The structure of schooling systems is not seen as a choice but as just plain common sense. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090084/figure-7-actions-concentrations-low-enough-to-prevent"><img alt="Actions concentrations low enough to prevent a temperature rise in excess of that level. The strategy also assumes that governments’ regulations can enforce such reductions at a cost their societies are willing to pay. In addition, the global-agreement strategy assumes that the world’s govern- ments can successfully negotiate an agreement in December 2009 that will inspire and compel future governments and the private sector to follow a path to a near—zero-emission economy. ‘These assumptions also seem vulnerable. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090085/table-2-source-filmer-hasan-and-pritchett-enrollment-of-year"><img alt="SOURCE: Filmer, Hasan, and Pritchett (2006, Table 2, p. 32). 2 Enrollment of 15-year-olds is assumed to be 100 percent in the grades covered by PISA. Cohort-Based Estimates of Cumulative Learning Achievement: Percentage Below MLGL or MLGH in Mathematics " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090086/table-3-source-gundlach-woessmann-and-gmelin-note-all"><img alt="SOURCE: Gundlach, Woessmann, and Gmelin (2001, Table A.2). NOTE: All testing compares Japanese students’ achievements with U.S. 13-year-olds’ achievement on that year’s NAEP for science. Japanese Student Achievement Compared with U.S. Student Achievement Table 3.2 Putting the evolution between 1970 and 1994 of test scores and expenditures per pupil together produces the truly shocking Table 3.3. In every OECD country, the changes in mea- sured learning achievement have been modest; in many countries, it has fallen, and in no country has learning achievement improved dramatically. At the same time, expenditures per pupil have risen substantially—if not sharply—in every OECD country. This means that the " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090089/table-3-source-adapted-from-gundlach-woessmann-and-gmelin"><img alt="SOURCE: Adapted from Gundlach, Woessmann, and Gmelin (2001, Tables 3.3 and 3.4, pp. 235-236). Achievement Stays the Same or Drops but Expenditures Rise in OECD Nations " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090091/table-4-source-adapted-from-das-and-zajonc-note-these"><img alt="SOURCE: Adapted from Das and Zajonc (2008). NOTE: These countries are estimated to round to zero above the threshold: Bahrain, Botswana, Chile, Cyprus, Ghana, Iran, Lebanon, Macedonia, Moldova, Morocco, Norway, Saudi Arabia, Philippines, South Africa, and Tunisia. Estimate of Students Above the Advanced Benchmark Score (625) in the Latest TIMSS Data " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090093/table-5-source-based-on-das-and-zajonc-test-score"><img alt="SOURCE: Based on Das and Zajonc (2008). Test- Score Distributions for Middle-Income Countries " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090095/table-6-note-all-the-key-assumptions-for-each-strategy-must"><img alt="NOTE: All the key assumptions for each strategy must prove true in order for that strategy to meet long-term climate-change goals. Thus, any key assumption that might plausibly fail represents a potential vulnerability of that strategy. 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This representation of RAND...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit <a href="http://www.rand.org/pubs/permissions.html" rel="nofollow">www.rand.org/pubs/permissions.html</a>. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-61443766-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-61443766-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382230/figure-2-suggests-how-an-an-analysis-campaign-can-affect"><img alt="Figure 2 suggests how an an analysis campaign can affect results. It recognizes that analysis should have both deep and shallow components: deep to assure that critical phenom- ena are understood and shallow to permit good communication, explanation, and persuasion. Policymakers and those receiving decisions need to understand the reasoning. This requires simple conceptual models and compelling stories (alternative models/stories in some cases). A feature of the analysis campaign is that it anticipates being able to report the top-level story (or conflicting stories) but also recognizes the need to generate in-depth explanation as neces- sary. This means zooming into detail as necessary to su pport findings and recommendations. " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382231/figure-2-some-other-features-of-an-analysis-campaign-should"><img alt="Some other features of an analysis campaign should be breadth of scope and methods and yf debate. Scope. A good analysis campaign should draw on diverse sources for information and ideas. Some of this happens naturally in good research organizations. We have in mind mod- els as diverse as statistical regressions, mathematical formulas, agent-based simulations, and live gaming. We also have in mind informing system concepts with insights from people as diverse as anthropologists, economists, and, urban planners. This suggests that study may be- gin with a divergent phase in which a broad view is taken of "the system," the questions, the possible ways of modeling, and the crucial uncertainties and disagreements not to be lost— including different conceptions of the system itself,’ Scope. A good analysis campaign should draw on diverse sources for information and " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382232/figure-3-illustrating-region-plots"><img alt="Figure 3 Illustrating Region Plots " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382237/figure-4-using-measure-of-regret-to-assess-an-option"><img alt="Figure 4 Using a Measure of "Regret" to Assess an Option's Robustness to Assumptions Note: Because more regret is "bad," a plan is better if it lies lower rather than higher in this chart. Earlier in the paper we mentioned that, historically, people (including decisionmakers) lave had a hard time dealing with contingent predictions, one of the problems afflicting de- vate about Limits to Growth. The problems lie deep in human psychology, but we know from Xperience that analysts and the senior leaders to whom they report can learn to think in erms of tradeoff charts such as in Figure 3 and Figure 4. They are then internalizing the con- ingent nature of outcomes. " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382241/table-1-uncertainty-analysis-to-better-confront-model"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472124/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382247/table-2-uncertainty-analysis-to-better-confront-model"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472124/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382265/table-3-current-type-of-question-asked-better-types-of"><img alt="Current Type of Question Asked Better Types of Questions for Policy Under Uncertainty by Policymakers It is not the role of analysis and analysts to create deus ex machina in the form of analyses that will reso ve our policy choices. That is rightly the job of elected officials and their ap- pointees. There are always ancillary questions of politics and policy, details and interests, that must as ama is illuminate ter of course also enter the policy deliberation process. But what analysts can do he policy trade-off space: which short-term actions (and preparation for later adaptations) will best achieve our long-term policy objectives despite broad uncertainties now and the ikelihood of unexpected developments as the future unfolds? 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Projecting likely futures has been view...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Public policy has always confronted future uncertainties. Projecting likely futures has been viewed as best practice for assessing proposed plans even though few would expect exactly those futures to occur. But in an era of deep uncertainties in which prior rules of thumb are no longer believed likely to hold true in years to come, sufficient diligence for policy analysis demands a different standard. • DMDU approaches collectively represent an evolving capacity to deal with the challenge of the future by providing a technology of complexity, especially in the analysis of problems in public policy. • New approaches to policy analysis may provide the means to enable policy processes better suited to deep uncertainty and dynamic change. And the recognition by policymakers that there exist (and that they should demand) new means for analysis that comport better to the emerging needs of policy would, in turn, allow more rapid diffusion of technique into realms not yet exposed to means for decisionmaking under deep uncertainty. • The interaction between analysts and policymakers requires contact between two distinct cultures. DMDU applications collectively present a body of theory and practice with the potential for providing a common vocabulary to the work of both analysts and of those charged with policy design and implementation during uncertain times. • For a problem requiring treatment by DMDU approaches, "any job worth doing is worth doing superficially." An analysis based on an initial fast and simple exploratory model will frequently elucidate many of the major interactions between policy choices and the problem system. (Exploratory Modeling is discussed extensively in Chaps. 2, 7, and 15.) Those factors that appear most salient may then be examined in more detailed fashion in later model revisions and recalibrations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cba01d6fc01c040225784dc72156c533" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74472093,"asset_id":61443783,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74472093/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61443783"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61443783"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61443783; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61443783]").text(description); $(".js-view-count[data-work-id=61443783]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61443783; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61443783']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "cba01d6fc01c040225784dc72156c533" } } $('.js-work-strip[data-work-id=61443783]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61443783,"title":"Reflections: DMDU and Public Policy for Uncertain Times","translated_title":"","metadata":{"publisher":"Springer International Publishing","ai_title_tag":"DMDU Strategies for Public Policy Uncertainty","grobid_abstract":"Public policy has always confronted future uncertainties. 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RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-61443772-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-61443772-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193099/figure-2-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193091/figure-1-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193112/figure-3-source-national-highway-transportation-safety"><img alt="SOURCE: National Highway Transportation Safety Administration, no date. RAND RR2333-S.1 Decades-Long Progress in Reducing Roadway Deaths Has Begun Reversing in the Past Five Years We Know Who Is Dying and Why " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193117/figure-4-sources-older-teen-young-adult-crashes-centers-for"><img alt="SOURCES: Older teen/young adult crashes: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, no date (b); Men: Insurance Institute for Highway Safety, 2017a; People in rural areas: NHTSA, 2017b; Population: Howden and Meyer, 2011. NOTE: Crash data for 2015; proportion of population from 2010 Census. RAND RR2333-S.2 Proportion of U.S. Roadway Deaths Relative to Proportion of Population " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193122/figure-5-these-three-approaches-are-essential-and"><img alt="‘These three approaches are essential and interconnected; none of the three will work effectively independent of the others. They are complementary, mutually dependent, and syn- ergistic. (See Figure S.3.) For example, a growing safety culture will foster safe behaviors, such as driving sober and within the speed limit, and create a strong market for advanced safety technologies (including automated vehicles). As people become accustomed to the safety benefits of advanced technology and improved roads, they will become less tolerant of risky behavior and more supportive of the changes needed to build a Safe System. ‘The effect of each change is intertwined with the others and mutually supportive—a “virtuous cycle.” " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193126/figure-6-sources-nhtsa-data-nhtsa-no-date-fars-data-census"><img alt="SOURCES: NHTSA, 2017c (2016 data); NHTSA, no date (FARS 2011 data); U.S. Census Bureau, 2016; Federal Highway Administration, 2016. RAND RR2333-1.7 Increases in Deaths by Road User Type, 2011 to 2016 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193129/figure-7-sources-older-teen-young-adult-crashes-cdc-national"><img alt="SOURCES: Older teen/young adult crashes: CDC, National Center for Injury Prevention and Control, no date (b); Men: IIHS, 2017a; People in rural areas: NHTSA, 2017b; Population: Howden and Meyer, 2011. NOTE: Crash data for 2015; proportion of population from 2010 Census. RAND RR2333-1.2 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193139/figure-8-sources-adapted-from-seppa-and-reason-rand-rr"><img alt="SOURCES: Adapted from Seppa (2013) and Reason (2000). RAND RR2333-1.3 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_008.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193144/figure-9-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_009.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193148/figure-10-source-adapted-from-curry-and-hodgson-rand-rr"><img alt="SOURCE: Adapted from Curry and Hodgson, 2008. RAND RR2333-A.1 Three-Horizon Foresight Provides a Framework for Visualizing Change over Time " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_010.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193149/figure-11-assumption-based-planning-helps-groups-find-hidden"><img alt="Assumption-Based Planning Helps Groups Find Hidden Assumptions and Weigh Risks 3 A more accurate—but vastly more confusing—representation of the curves associated with the First and Second Hori- zons would be a myriad of curves all slightly displaced, so that, over time, we could see supporting systems evolving with changing conditions and demands. These might be portrayed as being of different heights (that is, differing degrees of fit ness) at different points along the time line if changes in internal and external conditions overwhelm our ability to changs policies, institutions, and other structures swiftly enough to accommodate those changes. Figure A.2 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_011.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193152/figure-12-combining-hf-with-abp-weighs-both-future-visions"><img alt="Combining 3HF with ABP Weighs Both Future Visions and Alternative Strategic Pathways " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/figure_012.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193160/table-1-the-road-to-zero-vision-for-achieving-zero-roadway"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193162/table-2-sources-nhtsa-census-bureau-federal-highway"><img alt="SOURCES: NHTSA, 2017c; U.S. Census Bureau, 2016; Federal Highway Administration, 2016. Three Indicators of Roadway Deaths Have Worsened Since 2011 " class="figure-slide-image" src="https://figures.academia-assets.com/74472125/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/51193163/table-3-participants-in-the-three-worksops-the-people-listed"><img alt="Participants in the Three Worksops The people listed below participated in one or more of the three workshops. 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RDM was intended for use with formal models. However, we show a model-less RDM application to a portfolio planning problem (selecting U.S. Army security cooperation activities with a partner country) seeking to achieve several objectives. In the absence of formal models, the analysis tests candidate actions against different explicit statements of causal relationships to allow more systematic reasoning over choices and outcomes. Doing so renders assumptions about complex systems explicit, characterizes uncertainties in terms of effect on weighting policy choices rather than as presently unknowable probabilities, provides a venue for planners and evaluators to share findings and insights, and yields explicit expressions of theories of causation (TOC) that themselves act as formal models where none had previously existed. RDM allows important scenarios for decision planning to be generated analytically. 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This electronic representation of RAND intellectual property is provided for non-commercial use only. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. Permission is required from RAND to reproduce, or reuse in another form, any of our research documents for commercial use. For information on reprint and linking permissions, please see RAND Permissions.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-61443768-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-61443768-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090064/figure-1-of-the-desired-educational-outcomes-am-making-no"><img alt="of the desired educational outcomes (I am making no assumption that these can be adequately measured with standardized exams). Everyone’s actual educational goal is some preference over the distribution of completed learning—which could be that everyone be above some threshold or that the average be high, for example. Suppose we had an MLG based on a goal for minimally adequate learning (Filmer, Hasan, and Pritchett, 2006). One could then assume some cause-and-effect relationship between a measure of time spent in school (or grade pro- gression) on the horizontal axis and progress on the educational goal on the vertical axis and assume that the curricular design were such that completing the one (primary schooling, say) led to the other. Of course, assumptions can prove wrong, and there are three huge problems with connection between an MDG and an MLG. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090074/figure-2-disconnect-does-curricular-design-lead-to-the-mlg"><img alt="Disconnect 1. Does curricular design lead to the MLG at the MDG? " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090080/figure-3-distribution-of-test-scores-in-the-pisa-for"><img alt="Distribution of Test Scores in the 2003 PISA for Mathematics for Three Countries " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090081/figure-4-changes-in-full-time-wages-by-education-level-the"><img alt="Changes in U.S. Full-Time Wages, by Education Level, 1981-2005 ‘The third issue around the top end is globalization. One possible explanation is that returns to superstars increased because globalization allowed the highly skilled to leverage their talents over larger and larger markets. If that is the case, what this implies for the returns to a country’s economic elite that is globally low skilled is not clear. If they compete head to head (so to speak), they lower their wages or returns. This also may distort their relative returns so that they allocate into nontradable activities (e.g., government, law, medicine) and away from activities in which their skills are pitted head to head against global talent. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090082/figure-5-source-autor-katz-and-kearney-using-march-current"><img alt="SOURCE: Autor, Katz, and Kearney (2005), using March Current Population Survey (CPS) data, 1976-2004. NOTE: Statistics pool three years of data. College graduates are those with 16 or 17 years of completed schooling (surveys prior to 1992) or a baccalaureate degree only (1992 forward). RAND CF267-3.4 " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090083/figure-6-the-virtue-of-success-in-socialization-is-that"><img alt="the virtue of success in socialization is that complete success means the complete invisibility of inevitability: The structure of schooling systems is not seen as a choice but as just plain common sense. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_006.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090084/figure-7-actions-concentrations-low-enough-to-prevent"><img alt="Actions concentrations low enough to prevent a temperature rise in excess of that level. The strategy also assumes that governments’ regulations can enforce such reductions at a cost their societies are willing to pay. In addition, the global-agreement strategy assumes that the world’s govern- ments can successfully negotiate an agreement in December 2009 that will inspire and compel future governments and the private sector to follow a path to a near—zero-emission economy. ‘These assumptions also seem vulnerable. " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/figure_007.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090085/table-2-source-filmer-hasan-and-pritchett-enrollment-of-year"><img alt="SOURCE: Filmer, Hasan, and Pritchett (2006, Table 2, p. 32). 2 Enrollment of 15-year-olds is assumed to be 100 percent in the grades covered by PISA. Cohort-Based Estimates of Cumulative Learning Achievement: Percentage Below MLGL or MLGH in Mathematics " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090086/table-3-source-gundlach-woessmann-and-gmelin-note-all"><img alt="SOURCE: Gundlach, Woessmann, and Gmelin (2001, Table A.2). NOTE: All testing compares Japanese students’ achievements with U.S. 13-year-olds’ achievement on that year’s NAEP for science. Japanese Student Achievement Compared with U.S. Student Achievement Table 3.2 Putting the evolution between 1970 and 1994 of test scores and expenditures per pupil together produces the truly shocking Table 3.3. In every OECD country, the changes in mea- sured learning achievement have been modest; in many countries, it has fallen, and in no country has learning achievement improved dramatically. At the same time, expenditures per pupil have risen substantially—if not sharply—in every OECD country. This means that the " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090089/table-3-source-adapted-from-gundlach-woessmann-and-gmelin"><img alt="SOURCE: Adapted from Gundlach, Woessmann, and Gmelin (2001, Tables 3.3 and 3.4, pp. 235-236). Achievement Stays the Same or Drops but Expenditures Rise in OECD Nations " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090091/table-4-source-adapted-from-das-and-zajonc-note-these"><img alt="SOURCE: Adapted from Das and Zajonc (2008). NOTE: These countries are estimated to round to zero above the threshold: Bahrain, Botswana, Chile, Cyprus, Ghana, Iran, Lebanon, Macedonia, Moldova, Morocco, Norway, Saudi Arabia, Philippines, South Africa, and Tunisia. Estimate of Students Above the Advanced Benchmark Score (625) in the Latest TIMSS Data " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090093/table-5-source-based-on-das-and-zajonc-test-score"><img alt="SOURCE: Based on Das and Zajonc (2008). Test- Score Distributions for Middle-Income Countries " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_005.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/22090095/table-6-note-all-the-key-assumptions-for-each-strategy-must"><img alt="NOTE: All the key assumptions for each strategy must prove true in order for that strategy to meet long-term climate-change goals. Thus, any key assumption that might plausibly fail represents a potential vulnerability of that strategy. Key Assumptions Underlying Clean-Energy Revolution and Global-Agreement Strategies " class="figure-slide-image" src="https://figures.academia-assets.com/74472129/table_006.jpg" /></a></figure></div><div class="next-slide-container js-next-button-container"><button aria-label="Next" class="carousel-navigation-button js-profile-work-61443768-figures-next"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_forward_ios</span></button></div></div></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6824b2e69a9adab6489f7a44f051364d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":74472129,"asset_id":61443768,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/74472129/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="61443768"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="61443768"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61443768; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61443768]").text(description); $(".js-view-count[data-work-id=61443768]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61443768; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='61443768']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6824b2e69a9adab6489f7a44f051364d" } } $('.js-work-strip[data-work-id=61443768]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":61443768,"title":"Shaping Tomorrow Today: Near-Term Steps Towards Long-Term Goals","translated_title":"","metadata":{"publisher":"RAND Corporation","ai_title_tag":"Near-Term Steps for Achieving Long-Term Goals","grobid_abstract":"This document and trademark(s) contained herein are protected by law as indicated in a notice appearing later in this work. 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The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.</span></div><div class="wp-workCard_item"><div class="carousel-container carousel-container--sm" id="profile-work-61443766-figures"><div class="prev-slide-container js-prev-button-container"><button aria-label="Previous" class="carousel-navigation-button js-profile-work-61443766-figures-prev"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">arrow_back_ios</span></button></div><div class="slides-container js-slides-container"><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382230/figure-2-suggests-how-an-an-analysis-campaign-can-affect"><img alt="Figure 2 suggests how an an analysis campaign can affect results. It recognizes that analysis should have both deep and shallow components: deep to assure that critical phenom- ena are understood and shallow to permit good communication, explanation, and persuasion. Policymakers and those receiving decisions need to understand the reasoning. This requires simple conceptual models and compelling stories (alternative models/stories in some cases). A feature of the analysis campaign is that it anticipates being able to report the top-level story (or conflicting stories) but also recognizes the need to generate in-depth explanation as neces- sary. This means zooming into detail as necessary to su pport findings and recommendations. " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382231/figure-2-some-other-features-of-an-analysis-campaign-should"><img alt="Some other features of an analysis campaign should be breadth of scope and methods and yf debate. Scope. A good analysis campaign should draw on diverse sources for information and ideas. Some of this happens naturally in good research organizations. We have in mind mod- els as diverse as statistical regressions, mathematical formulas, agent-based simulations, and live gaming. We also have in mind informing system concepts with insights from people as diverse as anthropologists, economists, and, urban planners. This suggests that study may be- gin with a divergent phase in which a broad view is taken of "the system," the questions, the possible ways of modeling, and the crucial uncertainties and disagreements not to be lost— including different conceptions of the system itself,’ Scope. A good analysis campaign should draw on diverse sources for information and " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382232/figure-3-illustrating-region-plots"><img alt="Figure 3 Illustrating Region Plots " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_003.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382237/figure-4-using-measure-of-regret-to-assess-an-option"><img alt="Figure 4 Using a Measure of "Regret" to Assess an Option's Robustness to Assumptions Note: Because more regret is "bad," a plan is better if it lies lower rather than higher in this chart. Earlier in the paper we mentioned that, historically, people (including decisionmakers) lave had a hard time dealing with contingent predictions, one of the problems afflicting de- vate about Limits to Growth. The problems lie deep in human psychology, but we know from Xperience that analysts and the senior leaders to whom they report can learn to think in erms of tradeoff charts such as in Figure 3 and Figure 4. They are then internalizing the con- ingent nature of outcomes. " class="figure-slide-image" src="https://figures.academia-assets.com/74472124/figure_004.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382241/table-1-uncertainty-analysis-to-better-confront-model"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472124/table_001.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382247/table-2-uncertainty-analysis-to-better-confront-model"><img alt="" class="figure-slide-image" src="https://figures.academia-assets.com/74472124/table_002.jpg" /></a></figure><figure class="figure-slide-container"><a href="https://www.academia.edu/figures/19382265/table-3-current-type-of-question-asked-better-types-of"><img alt="Current Type of Question Asked Better Types of Questions for Policy Under Uncertainty by Policymakers It is not the role of analysis and analysts to create deus ex machina in the form of analyses that will reso ve our policy choices. That is rightly the job of elected officials and their ap- pointees. There are always ancillary questions of politics and policy, details and interests, that must as ama is illuminate ter of course also enter the policy deliberation process. But what analysts can do he policy trade-off space: which short-term actions (and preparation for later adaptations) will best achieve our long-term policy objectives despite broad uncertainties now and the ikelihood of unexpected developments as the future unfolds? 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