Magazine article AI Magazine

Sustainable Policy Making: A Strategic Challenge for Artificial Intelligence

Magazine article AI Magazine

Sustainable Policy Making: A Strategic Challenge for Artificial Intelligence

Article excerpt

Public policy issues cover a wide variety of fields: economy, education, environment, health, social welfare, and national and foreign affairs. They are extremely complex, occur in rapidly changing environments characterized by uncertainty, and involve conflicts among different interests. In the modern world, due to globalization, the political activity and intervention become more widespread, and so the effects of its interventions become more difficult to assess. At the same time it is becoming ever more important to ensure that actions are effectively tackling the real challenges that this increasing complexity entails.

Generally speaking, the policy-making process traverses four steps: policy planning, environmental assessment, implementation, and monitoring. The first three steps are performed ex-ante. In the planning step, strategic objectives are set, budget constraints are defined, geophysical constraints are considered. The assessment phase, which is traditionally performed after the planning step, concerns the evaluation of the impact of the policy plan on the environment, and to a certain extent on economy and society. Implementation consists of defining a set of instruments to support the planning objectives, such as incentives, information campaigns, tax exemption, and compulsion (to name a few). The monitoring step is performed ex-post, to check whether the implementation strategies achieve the expected objectives settled during the planning phase.

There are a number of problems in this process at present. First, the planning step and the environmental assessment are performed in sequence: in case a plan contains negative effects on the environment, only corrective countermeasures can be applied a posteriori. If planning and environmental assessment were performed at the same stage, an environmentally well-assessed plan could be produced instead. Second, the implementation instruments are decided without any proper strategy nor assessment of their effect on the society. These effects are indeed checked during the monitoring phase to measure whether they are conformant with the planning objectives in an ex-post fashion. Third, the steps are always performed manually with no (or very little) information and communications technology (ICT) support.

We strongly believe a number of AI techniques could be effectively used for aiding governance and policy making: the literature reports attempts to use agent-based simulation (Troitzsch et al. 1999), opinion mining (Pang and Lee 2008), visual scenario evaluation (Chamberlain et al. 2012), and optimization (Cattafiet al. 2011) to support specific cases of this process, but there is large space for improvement. What is totally missing at present is a comprehensive tool that assists the policy maker in all phases of the decision-making process. The tool should compute alternative scenarios each consisting of both a wellassessed plan and the corresponding implementation strategies to achieve its objective. We need a tool that is able to integrate and consider, at the same time, global objectives and individual/social reactions. These two perspectives could be, and often are, in conflict, and possibly game theory could be used to find an equilibrium between the two parts.

The schema we devise is depicted in figure 1. Moreover, the policy maker should take into account the global view of the policy, namely financial aspects, objectives, environmental impacts, and constraints and generate alternative scenarios. On the other hand, the society can participate in the policy-making process through e-participation both in the exante phase during the definition of the policy and in the ex-post phase for providing feedback on different scenarios. Clearly, we should be able to come out with an equilibrium between the global and the individual point of view. In this case game theory could play a role.

The ideas expressed in this article are a result of the EU FP7 project called ePolicy: Engineering the Policy Making Life Cycle which focuses on developing decision support systems for aiding policy makers across all phases of the policy-making process. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.