Collaborative environmental governance: Achieving collective action in social-ecological systems


A growing amount of empirical evidence shows the effectiveness of actors engaged in different collaborative governance arrangements in addressing environmental problems. However, studies also show that actors sometimes collaborate only as a means of advocating their own interests, while largely lacking a willingness to contribute towards jointly negotiated solutions to common problems. Hence, collaboration is sometimes unable to deliver any tangible outcomes, or merely produces symbolic outcomes such as aggregated wish lists where conflicts of interest are left untouched. Clearly, no single blueprint exists for how to succeed by using collaborative approaches to solve environmental problems. One way of approaching this puzzle is through the lenses of the participating actors and the ways in which they engage in collaboration with each other.

A Cognitive Model for Collaborative Agents

AAAI Conferences

We describe a cognitive model of a collaborative agent that can serve as the basis for automated systems that must collaborate with other agents, including humans, to solve problems. This model builds on standard approaches to cognitive architecture and intelligent agency, as well as formal models of speech acts, joint intention, and intention recognition. The model is nonetheless intended for practical use in the development of collaborative systems.

Real-Time Collaborative Planning with the Crowd

AAAI Conferences

Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.

Representing and Planning with Interacting Actions and Privacy

AAAI Conferences

Interacting actions — actions whose joint effect differs from the union of their individual effects — are challenging both to represent and to plan with due to their combinatorial nature. So far, there have been few attempts to provide a succinct language for representing them that can also support efficient centralized and distributed privacy preserving planning. In this paper we suggest an approach for representing interacting actions succinctly and show how such a domain model can be compiled into a standard single-agent planning problem as well as to privacy preserving multi-agent planning. We test the performance of our method on a number of novel domains involving interacting actions and privacy.