You can find my article on smart cities in the current issue of Practicing Planner.
Abstract: Within the past 24 months the concept of smart (and intelligent) cities has been become popular in the media. For instance, Scientific American ran a special issue on smart cities (September 2011). Industry players such as IBM and Siemens have specific programs and practices dedicated to advancing the cause of building smart cities. Despite its intuitive appeal, we have limited knowledge within the design, planning, and policy fields about the dimensions of the concept of smart cities, and limited practical experience regarding the barriers and potential opportunities. The term smart city is still new and appears to mean different things within different fields. In some ways the term is both complex and vague. Some experts use the term smart city to highlight advances in sustainability and greening of the city, while others use the term to portray infusion of information via technologies to better the lives of citizens. Even others consider the presence of high-level citizen engagement in the design and governance of the space as a key attribute of smarter cities. Therefore, no consensus exists within the academy on the characteristics of smart cities and how they fit within existing conceptual frameworks, such as sustainability and policy informatics. Although there is not yet consensus on a definition, I posit the following definition: A smart city is livable, resilient, sustainable, and designed through open and collaborative governance. The objective of this paper is to provide a preliminary conceptual framework for researchers, policymakers, and planners to apply in their design and development of smart cities. In light of the growing popular appeal of smart cities, I hope this essay will serve as a call to action for planners who must confront the day-to-day challenge of designing, developing, and retrofitting cities to make them smarter.
To access the article, please click here.
Erik Johnston (Arizona State University), Qian Hu (University of Central Florida), and I have completed a paper for the Governance of Complex Systems: Challenges of Making Public Administration and Complexity Theory Work Workshop to be held in Rotterdam, Netherlands in June 2011.
How the Application of Complexity and Policy Informatics to Public Administration can Change the Questions we Ask and the Solutions we Discover
We argue for introducing complexity theory to public administration because it allows us to exploit new connections, to raise new questions, and to explore innovative approaches to governance and management. To support more regular, effective, and defensible use of complexity as a contribution to policy-making, public administration scholars must continue to build supporting evidence. In this paper, first, we reflect on why existing analysis frameworks create structural blind spots for understanding governance practice. Second, using examples from our own research and professional experience we demonstrate that a complexity approach allows new questions to be asked that directly connect to policy problems and facilitate decision making in a cost effective manner. Third, we explore a number of factors including practical strategies and ethical concerns that may encourage or preclude the use of a complexity framework and method in policy settings. And finally, this paper calls on public administration scholars to be thoughtfully aware of and ethically responsible for the consequences of the use of complexity methods in research and practice.
Johnston, E., Desouza, K.C., & Hu, Q. “How the Application of Complexity and Policy Informatics to Public Administration can Change the Questions we Ask and the Solutions we Discover,” A Paper for Governance of Complex Systems: Challenges of Making Public Administration and Complexity Theory Work, Rotterdam, Netherlands, June 23-25, 2011.
My paper co-authored with Yuan Lin, Towards Evidence-Driven Policy Design: Complex Adaptive Systems and Computational Modeling, will appear in The Innovation Journal: The Public Sector Innovation Journal.
My second post on the Harvard Business Review site went live today! The post was written in collaboration with H. James Wilson and is titled, Finally, A Majority of Executives Embrace Experimentation. The post outlines the value proposition of building an experimentation culture within organizations and how executives can support employee experimentation.
The post has been picked up by Bloomberg Businessweek as well.
We would love to hear your comments on the ideas presented.
Along with my doctoral student, Yuan Lin, I have co-authored an article that describes how we might move towards evidence-driven policy design. This article draws from the keynote that I have at the 2010 Computational Social Science Society Conference.
Efforts to design public policies for social systems tend to confront highly complex conditions which have a large number of potentially relevant factors to be considered and rapidly changing conditions where continuous adaptation delays or obscures the effect of policies. Given unresolvable uncertainty in policy outcomes, the optimal solution is difficult, if ever possible, to nail down. It is more reasonable to choose a solution that is robust to as many future scenarios that might ensue from the decision. Arriving at such a solution requires policy makers to actively explore and exploit rich information to support their decision making in a cost-efficient, yet rigorous manner. We name this new working style as evidence-driven policy design and outline the characteristics of favorable evidence. We then argue that computational modeling is a potential tool for implementing evidence-driven policy design. It helps the study and design of solutions by simulating various environments, interventions, and the processes in which certain outcomes emerge from the decisions of policy makers. It allows policy makers to observe both the intended and, equally important, unintended consequences of policy alternatives. It also facilitates communication and consensus-building among policy makers and diverse stakeholders.
Yuan Lin, my doctoral student, and I have a paper accepted at the Thirty First International Conference on Information Systems. The paper describes our ongoing efforts to develop robust models for studying the dynamics of knowledge transfer within organizations.
Abstract: This study focuses on the co-evolution of informal organizational structures and individual knowledge transfer behavior within organizations. Our research methodology distinguishes us from other similar studies. We use agent-based modeling and dynamic social network analysis, which allow for a dynamic perspective and a bottom-up approach. We study the emergent network structures and behavioral patterns, as well as their micro-level foundations. We also examine the exogenous factors influencing the emergent process. We ran simulation experiments on our model and found some interesting findings. For example, it is observed that knowledgeable individuals are not well connected in the network, and our model suggests that being fully involved in knowledge transfer might undermine individuals’ knowledge advantage over time. Another observation is that when there is high knowledge diversity in the system, informal organizational structure tends to form a network of good reachability; that is, any two individuals are connected via a few intermediates.
Lin, Y.A, and Desouza, K.C. “Co-Evolution of Organizational Network and Individual Behavior: An Agent-Based Model of Interpersonal Knowledge Transfer,” In Proceedings of the Thirty First International Conference on Information Systems, St. Louis, Missouri (December 12-15, 2010).
I will be giving a keynote address at the 2010 Computational Social Science Society Conference (CSSS). CSSS 2010 is hosted by the Center for Social Dynamics and Complexity and the Consortium for Biosocial Complex Systems at. For more information on the conference, please click here [LINK].
From Hunches to Evidence Driven Policy Design: Leveraging Information through Simulation
Constructing public policy, whether at the national or local level, is a complex undertaking. Complexity arises from the number of stakeholders involved, varying agendas and incentives, resource constraints, a multitude of interacting variables, multiple time horizons, and even political climates. Due to these complexities, we too often categorize political and social problems as ‘wicked’ and unanalyzable. The default option is to take a haphazard approach to policy design, most often the outcome of the ego-based debates and negotiations of the decision-makers. In this keynote address, I will argue for a move from hunches (or intuition) to evidence driven policy construction. Today, due to the advancement of computational power and modeling techniques, we can simulate complex scenarios. Simulation gives us an ability to move policy construction from an activity primarily driven by historic case analysis and intuitions, to more of an applied science, where we can actually predict and control phenomenon. Through simulation we can, with reasonable certainty, ascertain the cost, benefit, risk, impact, and value proposition of a given policy. Using examples from simulation projects, such as a project that examined strategic options for dismantling terrorist networks, I will demonstrate how we can move policy design from being an ‘art’ to more of a ‘science.’