Global Economy Week 2

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Editor: Daniel Zeng, University of Arizona and Chinese Academy of Sciences, [email protected]

Technological advances in various computing fields, AI included, either have already had a dramatic impact or are perceived as potential game changers in many applications. Take big data and social media as cases in

point. As facilitators and the backbone of the emerging Science 2.0, they

Policy Informatics for Smart Policy-Making Daniel Zeng, University of Arizona and Chinese Academy of Sciences

promise to change how the scientific enterprise operates and innovates. In in- dustry settings, new waves of products and services based on these technolo- gies, many by startups touted as tomorrow’s Google and Facebook, are en- tering the marketplace, improving productivity in old industry sectors and opening up new opportunities for future businesses yet to be defined. In the public sector, these technologies are starting to make similarly profound im- pact and promise to potentially revolutionize policy-making.

Compared with applications stemming from the scientific community or private sector, public-sector applications tend to take a more conservative ap- proach (likely rightfully so) when adapting and adopting technology. As a result, it’s still too early to pinpoint full-scale redesign of policy-making ap- proaches in major public decision-making areas, or to discuss completed suc- cess stories. Yet, bits and pieces of the next generation of IT-driven policy- making have been emerging for some time.

It’s already common knowledge that social media provides great potential for policy makers to gauge public opinion. Researchers and analysts routinely study social media content, such as Twitter feeds, to characterize patterns of public sentiment on various issues with policy relevance, and in some cases, to identify how emotion or influence propagates through online social networks. Understanding gained through such analyses complements the traditional ap- proach, which is largely based on polling, and can inform policy-making.

In more specific public-sector application scenarios such as emergency re- sponse, a whole suite of information technologies, including but not limited to sensor networks and social media, are being integrated to provide faster and finer-granularity assessment of a situation. In addition, these technolo- gies serve as the enabling mechanism for coordination among people as well as resources, often in a distributed fashion, and provide timely feedback to deci- sion makers either during the planning phase or after the implementation of involved policies and decisions. In public health crisis management, most no- tably during the most recent Ebola epidemic, and the 2009 H1N1 (swine flu) pandemic, social media has been heavily studied, with all kinds of simulation- based predictive models developed. Despite the fact that most of these models performed poorly (and were often outrageously wrong), the public health com- munity has argued for the value of such models, from the policy standpoint.

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Policy Informatics Policy makers have traditionally relied on intuition, experience, small-sample human contact, polls, and media out- lets to gauge society’s “pulse.” As I ar- gued above, IT is providing innovative alternatives to improve and comple- ment the traditional approach. The field of policy informatics is emerg- ing to cross-fertilize between compu- tational sciences and public adminis- tration and policy, and to advance the framework of and infrastructural sup- port for public policy-making.

Current policy informatics research streams emphasize traditional infor- matics research in the context of pub- lic policy and administration. New do- main-specific information collection and analysis approaches are being de- veloped to meet the needs of complex policy and administration problems. Work on social media, population- scale big open data, and temporal- spatial-network visualization has re- ceived a lot of attention lately; more applied research investigates technol- ogy adoption issues in governance processes. Also gaining momentum is research on new data-driven decision- making models; complex systems view of governance; collective intelligence; behavioral studies of policy-makers, policy-making processes, and the pub- lic; and persuasive technologies.

From Informed Policy- Making to Smart Policy- Making Policy informatics is in its early stage of development, yet many of its concepts and techniques have already met with success in the policy re- search community and in practice. It’s safe to characterize the state of the art as being largely informatics and data science-based, focusing on better situ- ational awareness.

In this sense, policy informatics is already using technological means

to enable informed policy-making. Granted, providing principled in- formed policy-making frameworks and tools to policy makers can have enormous implications; concerted ef- forts from multiple disciplines are still needed in the years and decades to come, to perfect such frameworks and tools, and promote their adop- tion. In the meantime, it makes a lot of sense for the research community to look beyond informed policy-mak- ing to explore how policy informat- ics can help build the foundation for even better and more advanced pol- icy-making, something more akin to smart policy-making.

Of course, smart policy-making is built on top of informed policy-mak- ing, which promises effective, real- time situational awareness and anal- ysis capabilities to make use of data. What advanced capabilities, then, will differentiate smart policy-making? To come up with a definitive set of such differentiators won’t be possible due to the topic’s emerging nature. But from the current literature and on- going discussions among academics and practitioners, it isn’t too difficult to venture on some novel aspects or even pillars of next-generation smart policy-making:

• Informed policy-making focuses on what has happened and what is happening. Smart policy-making needs to take into consideration additional information, such as what might happen down the road. In other words, smart policy-mak- ing entails a much more proactive framework.

• In informed policy-making, data processing tends to be treated as an independent capability, emphasiz- ing various engineering aspects of data sharing and mining. In smart policy-making, the integration be- tween data and the domain is ex-

pected to be much tighter. Various kinds of behavioral, affect, and root-cause analyses, at both the in- dividual and group/population lev- els, would need to be carried out in particular policy contexts.

• With a significantly improved un- derstanding of the policy environ- ment, and much more detailed, data-driven models, we can expect possible major changes as to the framing, decision-making frame- work, and evaluation mechanism of policy alternatives. As such, it’s conceivable that new decision-mak- ing tools, in addition to informat- ics and situational awareness tools, will play a crucial role in smart policy-making.

Since the development of policy informatics, AI has been a major contributor. A significant portion of technical work supporting informed policy-making can be classified as applied AI research. Smart policy- making is expected to create more exciting and novel research prob- lems for AI researchers, challeng- ing the start-of-the-art predictive analytics, behavioral analysis, and multi-agent simulation, among oth- ers. In an era full of opportunities for A I researchers in applications such as self-driving cars, robotics, Siri, and Cortana, it’s important for the AI community not to forget pol- icy-making as a fruitful area with great potentials.

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