As such, there is an eagerness to see the emergence of technology that will enhance collaboration in the teleworking environment. One early example and avenue is the creation of a hybrid app store that houses both internally developed business-to-employee (enterprise) apps and externally developed third party apps.  This type of platform would allow businesses to distribute company-developed apps to employees, however one challenge still prevalent is the balance between increased user-accessibility and adequate security.   Accompanying the hybrid app store development is a movement towards apps that focus on not only the dissemination of information, but also improving the capacity for collaboration.

With a different type of work environment evolving, the government is looking for a new mix of technology and innovations to ensure that the virtual worker can maintain hyper-productivity and availability; all while lowering agency operational costs. The current cost-savings are reflected in the 2011 Telework Status report to Congress by the Office of Personnel Management (OPM), which finds that in the federal government “33 agencies reported cost savings/benefits as a result of telework; of these, the greatest benefit was in the area of productivity (39%), then human capital, such as recruitment and retention (37%), and realized savings in leave (34%).” As a workforce emerges that is highly connected with technologically boosted efficiency and performance, time, location, and potential disasters seem to be shrinking as barriers to business-as-usual.  The impact of this Act and the resulting technology has effects larger than workforce adaptation and reduced operational costs; changes can be expected in the broader hiring practices, ethics, IT spending and morale of the federal government and its employees, as noted by Camille Tuutti.

Do you agree with these potentially larger impacts? Do you see any other effects on the workplace by a push towards more virtual workers?  Are there any teleworking obstacles that cannot be addressed by technological innovations?  Do you think hybrid app stores are the next big step for companies?  If not, what other workplace innovations do you think we will see in 2012?

With a federal mobile strategy that looks to capitalize on technology and technological innovations, increased citizen engagement and federal worker productivity, as well as improved delivery of government information/services and reduced operational expenditures are anticipated outcomes.  All in all, the draft strategy and input shows a definite direction for federal agencies; movement towards mobile technology and finding ways to balance the needs of innovation and security.

Streamlining and consolidation of this technology in an ‘app store’ seems like the next big step for the federal government in 2012, as made clear through participation on the web dialogue.  Moreover, the impact of investment in developing reusable assets across the federal agencies will have a definite influence on the larger commercial marketplace, notes Henry Kenyon, ultimately expanding the range of software and engineering opportunities.

Your thoughts? Do you think that an ‘app store’ would increase and improve inter-agency collaboration?  What types of apps do you see being most effective in meeting efficiency and operational goals?  Do you see any major obstacles to mediating issues of accessibility and security when it comes to open data programs for app development?

In every part of the world and at every organizational level of society, rapidly accelerating changes in conditions from the very local to global require us to make urgent personal and public policy decisions about the highly complex coupled social-ecological-economic system in which we live. Due to inherent complexity, models using current knowledge under specific limited assumptions and conditions are essential tools for making decisions to lead us on paths to a sustainable future.

Models are fundamental to human thinking and functioning. They are approximations of the behavior of things and reflect perceptions of patterns and efforts to categorize, explain, and predict future behavior of physical, biological, social, and economic phenomena and systems. Models are essential in organizing and interpreting information, whether implicit and intuitive, or elaborate mathematical constructs, and they serve as engines of simulations and games. In games and simulations, they provide experience in considering and analyzing multiple possible outcomes dependent on input assumptions and knowledge.

Yet models and modeling per se are nearly invisible in most educational curricula from elementary school through university. Consequently, most individuals from young students to government ministers have little experience or understanding of the strengths, limitations, and modes of use of models for gaining insights and making decisions.

How can the capacity to understand and effectively use models to interpret the massive flow of information to guide policy makers and citizens be strengthened? That is the central question of this chapter, which is intended to stimulate thinking and action to build this capacity.

Innovation Policy Modeling with SKIN

This chapter introduces an agent-based model to simulate the effects and impacts of policy making on the structure, composition and outputs of research and innovation networks. The model allows policy makers to examine the contributions of specific research and innovation policies to societal goals. Additionally, by varying the ‘virtual’ policies, policy makers can evaluate the impacts of different policy levers, judge the effects of policy decisions taken, and make ex-ante evaluations of future policies.

Defining Policy Informatics Kevin C. Desouza and Erik Johnston

Today, the nature of public agencies and governance processes are undergoing fundamental changes due to advancements in information and computational technologies. Technologies have enabled us to leverage information in ways that previously impractical. Solving complex governance problems, dilemmas, and challenges requires deliberate, and sophisticated, information analysis. In this paper, we lay out a vision for policy informatics. Policy informatics is the study of how information is leveraged and efforts are coordinated towards solving complex public policy problems. Driven by the need to exploit information to tackle complex policy problems and to ensure efficient and efficient policy setting and implementation platforms, governance informatics seeks to 1) enhance policy analysis and design through visualizing, modeling, and simulating complex policy scenarios, 2) study the role of information systems and information-based governance platforms in policy planning, deliberation, and implementation, and 3) advance the management of information systems projects in the public sector.

Charting a Future of Policy Informatics
Erik Johnston and Kevin C. Desouza

“We are arguably in a golden age of computer modeling for policy analysis. Computational power is plentiful. A plethora of models are in use or under development, from small spreadsheet models developed for individual use to large multipurpose supercomputer-based simulations. Computer modeling has become central to nearly every kind of policy concern, from military procurement, to economic forecasting, to studies of global climate change. If computer modeling were fulfilling its promise this would also be a golden age for policy analysis with the computational power of computer models supporting significantly better decision making than was possible thirty years ago.” (Bankes, 1993, pg. 435)[1]

This quote is as true today as it was back in 1993. We have at our disposal the most sophisticated tools ever created and we organize, communicate, and administer more complex policies than ever before. What has changed is the timeframes that we have to master and become comfortable with current technology before it is antiquated. Technology generations are shorter than ever and the skills we need are not mastering any one technology, but becoming comfortable in a changing space that we will never fully comprehend.

In this concluding chapter, we will bring together key messages and lessons learnt from the previous chapters. We will use these as a springboard to chart out a future trajectory for the field. Issues covered will include building a community of practice around policy informatics, the sharing of data and methods, collaborative engagement with practitioners, and even the mentoring of the next generation of scholars.

While there are many good reasons to build computational models of social systems, in this chapter I focus on one:  computational models can lead to tangible products that can be used as vehicles for stakeholder learning, engagement, and action.   More specifically, I describe my experiences developing and delivering learning environments that use system dynamics-based board games as catalysts for organizational performance improvement in the manufacturing and information technology industries.  Drawing on both the lessons learned from this “school of hard knocks” and extant theory, I present a framework for designing simulation-based learning environments that lead to real world action.

Picture from a workshop where I used The Project Value Game with a group future charter school founders.

The authority of science depends upon the ability of researchers to draw sound inferences from evidence. However, what constitutes sound evidence for policy inquiry is not well defined nor well understood. We draw upon how evidence is viewed in the social, physical and natural sciences and the law to discuss the many meanings of evidence. We discuss how these meanings relate to evidence for inquiry in the policy informatics context and argue that a single set of principles or rules cannot apply in all contexts. We consider whether rules of evidence as discussed in legal contexts are better suited for policy informatics than those commonly used in scientific research. We draw upon lessons learned from research, teaching and practitioner decision support to develop a framework and heuristics for evaluating evidence for use in policy informatics.

Classification Inference & Event Attribution

A major objective of both academic and field research on violent groups is to devise a classification system that captures ideological, methodological, and behavioral relationships among them. In the last decade, development of new models incorporating thinking from international relations, computation, and area studies were actively solicited by security agencies in the U.S. and other countries. Our own interest in this topic led to some work on development of cross-disciplinary policy informatics work in the comparison and classification requisite for making higher-level observations about nature of these groups and even tactics that may be used to manage or mitigate their activities.

Lack of detailed information about the inner workings of violent groups, the large number of groups that exist, and the wide array of different types of groups are major obstacles to the construction of meaningful classifications. In this chapter, we present a methodology based on hierarchical clustering that uses entirely open information sources to construct a complete ontology over a set of violent groups. We formalize the History-Biased Event Participant Inference Problem identifying the violent groups most likely involved in a given event and then present a methodology that solves this problem. Our approach trained a naïve Bayesian model of violent group behavior using a data set of open-source events with known group involvement.  This model is then used to identify groups whose behavioral model is most consistent with an event for which all participants (or culpable parties) are unknown.

In order to test our method, we used a data set provided by the Institute for the Study of Violent Groups (ISVG) which consisted of attributes of and responsible groups for over 28,000 violent events reported in open-source news sources.  Collected events spanned a five year period from January 1, 2002 to December 31, 2007. We used our method to perform 10-fold cross-validations on this data set and determined that our method predicts true participants with over 80% accuracy. These results both validate the utility and performance of our method and reaffirm the ability for open-source intelligence to offer new insights into violent and clandestine group behavior.

We studied our method’s performance against data collected during the November 2008 Mumbai attacks. Using models constructed from the ISVG dataset and attributes of the attacks taken from news reports released directly following the incident, This case study, in particular, suggests that our method can provide valuable insights to those studying violent group behavior. We finally summarize the issues involved in the problem of policy informatics on rapidly evolving policy problems, in this case transnational terrorism, and the difficulties of joining generalized international relations method with computation and complex area studies knowledge.

A major objective of both academic and field research on violent groups is to devise a classification system that captures ideological, methodological, and behavioral relationships among them. In the last decade, development of new models incorporating thinking from international relations, computation, and area studies were actively solicited by security agencies in the U.S. and other countries. Our own interest in this topic led to some work on development of cross-disciplinary policy informatics work in the comparison and classification requisite for making higher-level observations about nature of these groups and even tactics that may be used to manage or mitigate their activities.

Lack of detailed information about the inner workings of violent groups, the large number of groups that exist, and the wide array of different types of groups are major obstacles to the construction of meaningful classifications. In this chapter, we present a methodology based on hierarchical clustering that uses entirely open information sources to construct a complete ontology over a set of violent groups. We formalize the History-Biased Event Participant Inference Problem identifying the violent groups most likely involved in a given event and then present a methodology that solves this problem. Our approach trained a naïve Bayesian model of violent group behavior using a data set of open-source events with known group involvement.  This model is then used to identify groups whose behavioral model is most consistent with an event for which all participants (or culpable parties) are unknown.

In order to test our method, we used a data set provided by the Institute for the Study of Violent Groups (ISVG) which consisted of attributes of and responsible groups for over 28,000 violent events reported in open-source news sources.  Collected events spanned a five year period from January 1, 2002 to December 31, 2007. We used our method to perform 10-fold cross-validations on this data set and determined that our method predicts true participants with over 80% accuracy. These results both validate the utility and performance of our method and reaffirm the ability for open-source intelligence to offer new insights into violent and clandestine group behavior.

We studied our method’s performance against data collected during the November 2008 Mumbai attacks. Using models constructed from the ISVG dataset and attributes of the attacks taken from news reports released directly following the incident, This case study, in particular, suggests that our method can provide valuable insights to those studying violent group behavior. We finally summarize the issues involved in the problem of policy informatics on rapidly evolving policy problems, in this case transnational terrorism, and the difficulties of joining generalized international relations method with computation and complex area studies knowledge.

The authors wish to enter a chapter on the practical lessons of policy informatics research, drawing upon their experience in the last decade. This chapter would provide views on several areas, including: (1) the linkage between scholarship and practitioner input on the research agenda of computing and politics; (2) the problem of discontinuity in data, produced by dramatic shifts in global social and political organization, enabled by globalization and the Internet; and (3) the issues of overlapping and orthogonal relationships regarding expertise and input in which computational methodology and political subject matter merge.

Policy Informatics – Reflections from User Observations

Presented are our lessons learned from several projects.  Our primary focus is in the domain of international relations (IR), however, we accept that barriers and sovereignty are concepts grossly redefined by the explosion in digital connectivity around the globe in the past two decades.  We differentiate here between policy informatics, which we would generally argue to be the application of computational or algorithmic methods to policy or political research and studies in information policy, which may take form in more conventional quantitative or qualitative form. We assert that, research programs that apply computational method to better understand political phenomena are a rapidly developing area of scholarship. That said, significant obstacles exist in the study of political behavior by algorithmic rather than statistical process. American political science has dedicated significant effort to the understanding of IR phenomena through the study of data regarding the actions of states via statistical means. Two major developments challenge this approach to IR scholarship, the creation of the Internet and user-driven communications platforms (i.e. Twitter, Facebook and YouTube), and the capacity to rapidly develop software designed to study new sets of structured and unstructured data.

Our own experience draws from several subjects: the institutional Wiki platform at the U.S. Department of State, Diplopedia; transnational terror group actions and behavior; Internet filtering activities; and the aggregation of attention in political discourse and crisis on the Twitter social media platform. These our major collaborative projects have taught us important and painful lessons on the study of revolution, in this case an Information Revolution, which we believe to be ongoing.  We aim to illustrate a path to policy informatics scholarship that we have pursued and where we believe interesting work is to be found.

Policy problems, alternatives, and decisions address myriad topics and issues. They can focus on education, public health, transportation, urban design, taxation, child welfare, economic development and many other areas. Each policy focus has its own substantive considerations, expertise, and conflicts. But all instances of policy analysis have one common element — they need, use, and generate information. Information is often treated as a black box in policy making. Stakeholders, analytical techniques, and technology tools all receive considerable attention. But information is often treated as a given; used uncritically; trusted without examination. However, the ways in which information is embedded in practical contexts influence its fitness and usability for analytical purposes.  At the same time, because information flows tie the parts of complex social systems together, information itself could be a valuable focus for analysis and for policy alternatives.  This chapter will consider the nature, value, and challenges of information as an essential component of the policy analysis process, drawing on examples in such areas as human services, land records, and financial transparency. It concludes with a conceptual model of the key relationships among policies, organizational considerations, technology, and data, and the practical context in which they interact.

The pathology of American schools is that they know how to change. They know how to change promiscuously and at the drop of a hat. What schools do not know how to do is to improve, to engage in sustained and continuous progress toward a performance goal over time. Elmore, R. (2002). The Limits of “Change”

A cursory look at the literature shows that education research suffers from a surfeit of conflicting and overlapping, untestable and implicit theories used to guide policy actions, often in support of conflicting pre-assumed epistemological or political points of view. These theories assume that innovations, once validated, can be faithfully taken to scale.  But all education is, at its core, dependent on local conditions. The number of theories and their implicit and ad-hoc nature conflict with the need to inform and learn from the sustainable evolution of educational organizations with their multifaceted complexity of actors, stakeholders, environments, and resources and capacity for action.

For many, educational informatics focuses exclusively on information about student achievement. From investments made that both aggregate and allow for disaggregation of student results we learn little about the functioning of the system itself, how it does or does not learn, and how it can counteract the pressures to revert to a stable nonoptimal state.

We look to policy informatics’ use of dynamical simulations and modeling as a tool to empower policymakers themselves test and improve policy strategies before putting them in practice, and to explore both their unanticipated consequences and unrealized opportunities. Introducing “what if” experimental scenarios can bring into policymaker decisions tests of plausible theories of action—the production of successes and failures—and an experimental ethos not possible without the tools of policy informatics.

For example, educators and education researchers look at concepts like interest to ask how students become interested in topics like science. We ask instead, from a policy perspective, “How does a community produce a population of students with a distribution of levels of interest in science, with some being very interested, and many others being uninterested?” Mechanisms of success and failure will certainly use information from the psychological realm and student achievement, but will also have to include organizational learning, feedback loops, timelines, sensitivity to initial conditions, and variability in those conditions—all aspects of complexity—to get an accurate picture of how the system can evolve.