Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0007
William B. Rouse
This chapter focuses on the health and well-being of people, looking at how health services are provided in the U.S. delivery ecosystem. Not surprisingly, the emphasis of this chapter is on model-based approaches to support decision-making. Computational modeling can substantially contribute to exploring possible futures for health and well-being. Patient, provider, and payer data sets can be used to parameterize these computational models. Large interactive visualizations can enable a wide range of stakeholders to participate in exploring possible futures. Policy flight simulators can enable projecting likely impacts of policies, for example, alternative payment schemes, before they are deployed. There is enormous variety in healthcare, including patients, providers, and payers, as well as the economic and social circumstances in which they operate. Computational models can be invaluable for projecting the impacts of this variety and considering how both system designs and policy designs should be tailored.
{"title":"Health and Well-Being","authors":"William B. Rouse","doi":"10.1093/oso/9780198846420.003.0007","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0007","url":null,"abstract":"This chapter focuses on the health and well-being of people, looking at how health services are provided in the U.S. delivery ecosystem. Not surprisingly, the emphasis of this chapter is on model-based approaches to support decision-making. Computational modeling can substantially contribute to exploring possible futures for health and well-being. Patient, provider, and payer data sets can be used to parameterize these computational models. Large interactive visualizations can enable a wide range of stakeholders to participate in exploring possible futures. Policy flight simulators can enable projecting likely impacts of policies, for example, alternative payment schemes, before they are deployed. There is enormous variety in healthcare, including patients, providers, and payers, as well as the economic and social circumstances in which they operate. Computational models can be invaluable for projecting the impacts of this variety and considering how both system designs and policy designs should be tailored.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131798172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0004
W. Rouse
This chapter focuses on humans as consumers of products and services as well as participants in designing these products and services. Human-centered design is a process of considering and balancing the concerns, values, and perceptions of all the stakeholders in a design. Attributes of importance to stakeholders, and the functions that provide these attributes, can be modeled and explored to plan new products and services. Modeling tools that are independent but share representations and data can enable the formulation and exploration of these models. Model-based analyses should compare planned offerings to those of the competition, including the likely reactions of competitors to these offerings. The status quo is compelling because the customer already has it and knows how to use it, and it requires little if any additional expenditures.
{"title":"Markets and Competitors","authors":"W. Rouse","doi":"10.1093/oso/9780198846420.003.0004","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0004","url":null,"abstract":"This chapter focuses on humans as consumers of products and services as well as participants in designing these products and services. Human-centered design is a process of considering and balancing the concerns, values, and perceptions of all the stakeholders in a design. Attributes of importance to stakeholders, and the functions that provide these attributes, can be modeled and explored to plan new products and services. Modeling tools that are independent but share representations and data can enable the formulation and exploration of these models. Model-based analyses should compare planned offerings to those of the competition, including the likely reactions of competitors to these offerings. The status quo is compelling because the customer already has it and knows how to use it, and it requires little if any additional expenditures.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129374338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0003
W. Rouse
Chapter 3 address the higher-education cost bubble, why it is unsustainable, and the ways it will likely burst, using a computational model of research universities to explore possible futures for these universities. It is not possible to predict what mix of the scenarios of interest will actually emerge, although the forces driving these changes are already evident. Universities need strategies and investments that enable robust responses to whatever mix of scenarios emerges. The higher-education cost bubble will inevitably burst, probably facilitated by increasingly powerful and sophisticated technology platforms. Universities need the right portfolio of investments in the hedges that will assure success despite the bursting of the bubble. Without such changes, many institutions of higher education will disappear amidst this “creative destruction.”
{"title":"Economic Bubbles","authors":"W. Rouse","doi":"10.1093/oso/9780198846420.003.0003","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0003","url":null,"abstract":"Chapter 3 address the higher-education cost bubble, why it is unsustainable, and the ways it will likely burst, using a computational model of research universities to explore possible futures for these universities. It is not possible to predict what mix of the scenarios of interest will actually emerge, although the forces driving these changes are already evident. Universities need strategies and investments that enable robust responses to whatever mix of scenarios emerges. The higher-education cost bubble will inevitably burst, probably facilitated by increasingly powerful and sophisticated technology platforms. Universities need the right portfolio of investments in the hedges that will assure success despite the bursting of the bubble. Without such changes, many institutions of higher education will disappear amidst this “creative destruction.”","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129856167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0005
Rashid A. Khan, Hassan Qudrat-Ullah
This chapter discusses how to attach value to technology options. One needs to address the balance between investing in getting better at what you are already doing versus investing in doing new things. There are numerous high-profile examples of companies unsuccessfully addressing this balance, leading to their eventual demise. Technology and process investments create contingent opportunities for later solutions; they are contingent in the sense that they may not emerge. Many markets are inherently laced with uncertainties. If a company is better at managing uncertainty than its competitors are, high profits can result. Strategic value is the sum of the net present value of current lines of business, and the net option value of aspirational lines of business. Options-based thinking can provide a framework for value-centered organizations by characterizing value, assessing value, and managing value.
{"title":"Technology Adoption","authors":"Rashid A. Khan, Hassan Qudrat-Ullah","doi":"10.1093/oso/9780198846420.003.0005","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0005","url":null,"abstract":"This chapter discusses how to attach value to technology options. One needs to address the balance between investing in getting better at what you are already doing versus investing in doing new things. There are numerous high-profile examples of companies unsuccessfully addressing this balance, leading to their eventual demise. Technology and process investments create contingent opportunities for later solutions; they are contingent in the sense that they may not emerge. Many markets are inherently laced with uncertainties. If a company is better at managing uncertainty than its competitors are, high profits can result. Strategic value is the sum of the net present value of current lines of business, and the net option value of aspirational lines of business. Options-based thinking can provide a framework for value-centered organizations by characterizing value, assessing value, and managing value.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123825184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0010
W. Rouse
This chapter discusses the nature of exploring possible futures, not in terms of case studies, but with a higher-level view of the overall process. Six themes—investments, stakeholders, change, decisions, design, and prediction—are reflected in most of the key points in this book. Clearly, exploring possible futures concerns investments in pursuing these futures involving multiple stakeholders and significant change. Models are employed to frame decisions, assess alternative designs, and make predictions about what might happen. Typical explorations involve planning new generations of existing product lines and new application domains for existing technology capabilities, assessing technology options for enabling new offerings, and addressing major enterprise challenges. Explorations should begin by focusing on desirability in terms of the “preference space” of major stakeholders. At the same time, avoid consideration of feasibility in the sense of what is achievable in the “physical space” of the domain of interest.
{"title":"Exploring Possible Futures","authors":"W. Rouse","doi":"10.1093/oso/9780198846420.003.0010","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0010","url":null,"abstract":"This chapter discusses the nature of exploring possible futures, not in terms of case studies, but with a higher-level view of the overall process. Six themes—investments, stakeholders, change, decisions, design, and prediction—are reflected in most of the key points in this book. Clearly, exploring possible futures concerns investments in pursuing these futures involving multiple stakeholders and significant change. Models are employed to frame decisions, assess alternative designs, and make predictions about what might happen. Typical explorations involve planning new generations of existing product lines and new application domains for existing technology capabilities, assessing technology options for enabling new offerings, and addressing major enterprise challenges. Explorations should begin by focusing on desirability in terms of the “preference space” of major stakeholders. At the same time, avoid consideration of feasibility in the sense of what is achievable in the “physical space” of the domain of interest.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122207029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0008
W. Rouse
This chapter addresses the ways in which human intelligence in routine and partly routine jobs can be augmented rather than replaced. The design of aids should begin with defining the user experience, proceed to designing the user interface to support this experience, and then focus on the enabling technologies. Intelligent aids should be considered for enhancing human performance; the extent of success will depend on the domain of application and the potential performance of the aid. Intelligent aids are inherently model based, drawing upon symbolic logic, mathematical paradigms, and/or statistical models; understanding the underlying modeling assumptions is key to establishing confidence in and trust of such aids. Intelligent systems technology has much promise but also many perils that warrant attention; its prospects depend on well-reasoned strategies for development and adoption.
{"title":"Intelligent Systems","authors":"W. Rouse","doi":"10.1093/oso/9780198846420.003.0008","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0008","url":null,"abstract":"This chapter addresses the ways in which human intelligence in routine and partly routine jobs can be augmented rather than replaced. The design of aids should begin with defining the user experience, proceed to designing the user interface to support this experience, and then focus on the enabling technologies. Intelligent aids should be considered for enhancing human performance; the extent of success will depend on the domain of application and the potential performance of the aid. Intelligent aids are inherently model based, drawing upon symbolic logic, mathematical paradigms, and/or statistical models; understanding the underlying modeling assumptions is key to establishing confidence in and trust of such aids. Intelligent systems technology has much promise but also many perils that warrant attention; its prospects depend on well-reasoned strategies for development and adoption.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131041757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0002
W. Rouse
Chapter 2 addresses elements of modeling. Modeling efforts should be driven by the questions that prompted the efforts and the phenomena associated with addressing the questions of interest. Visualizations of the phenomena of interest can help to both engage stakeholders and simplify the trade-offs that need to be addressed. There are a variety of representational paradigms that can be employed to model the evolution of the states of the system of interest. Choices among representations depend on the specific nature of the phenomena of interest, the data available to support use of this representation, and the expertise and preferences of the personnel involved. Computational models—and visualizations—are means to explore phenomena of interest, identify central trade-offs, and foster collaborative solutions. Decision-making teams use models to gain insights into the complexity of their broadly defined enterprise, with an overarching goal of understanding what might happen.
{"title":"Elements of Modeling","authors":"W. Rouse","doi":"10.1093/oso/9780198846420.003.0002","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0002","url":null,"abstract":"Chapter 2 addresses elements of modeling. Modeling efforts should be driven by the questions that prompted the efforts and the phenomena associated with addressing the questions of interest. Visualizations of the phenomena of interest can help to both engage stakeholders and simplify the trade-offs that need to be addressed. There are a variety of representational paradigms that can be employed to model the evolution of the states of the system of interest. Choices among representations depend on the specific nature of the phenomena of interest, the data available to support use of this representation, and the expertise and preferences of the personnel involved. Computational models—and visualizations—are means to explore phenomena of interest, identify central trade-offs, and foster collaborative solutions. Decision-making teams use models to gain insights into the complexity of their broadly defined enterprise, with an overarching goal of understanding what might happen.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114849929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-12DOI: 10.1093/oso/9780198846420.003.0006
W. Rouse
This chapter focuses on the operations and maintenance of new product and service offerings once they have been deployed; in particular, it addresses dealing with system failures. Addressing system failures is an important aspect of operating and maintaining complex systems, particularly when laced with behavioral and social phenomena. Despite advances in technology and automation, humans will inevitably have roles in addressing failures when detection, diagnosis, and compensation cannot be automated. Human problem-solving involves a mix of pattern recognition and structural sleuthing based on mental models for taskwork and teamwork. Training and aiding can enhance human problem-solving performance by fostering problem-solving strategies and tactics, as well as team coordination.
{"title":"System Failures","authors":"W. Rouse","doi":"10.1093/oso/9780198846420.003.0006","DOIUrl":"https://doi.org/10.1093/oso/9780198846420.003.0006","url":null,"abstract":"This chapter focuses on the operations and maintenance of new product and service offerings once they have been deployed; in particular, it addresses dealing with system failures. Addressing system failures is an important aspect of operating and maintaining complex systems, particularly when laced with behavioral and social phenomena. Despite advances in technology and automation, humans will inevitably have roles in addressing failures when detection, diagnosis, and compensation cannot be automated. Human problem-solving involves a mix of pattern recognition and structural sleuthing based on mental models for taskwork and teamwork. Training and aiding can enhance human problem-solving performance by fostering problem-solving strategies and tactics, as well as team coordination.","PeriodicalId":415137,"journal":{"name":"Computing Possible Futures","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122970432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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