Review of the Returns to ACIAR's Bilateral R&D Investments

Research and Development/Tech Change/Emerging Technologies,

Special issue on water economics and policy

Resource /Energy Economics and Policy,

Monitoring of compliance in Australian conservation contracts

Government and non-government conservation agencies have long-term goals and objectives to provide environmental services, such as conserving the biodiversity of Australian native vegetation. In addition to national parks and reserves, private lands are often included in conservation programs to achieve these objectives. Formal contracts are entered into between the private landholder and the conservation agency to provide environmental services, or more commonly to provide inputs that are likely to lead to environmental services. The paper examines the costs and benefits of monitoring these conservation contracts when biodiversity change is stochastic.conservation, compliance, monitoring, enforcement, environmental regulation, Environmental Economics and Policy,

Extension and Outreach: Not a Question of If, but How

In this article, the authors develop a conceptual framework for effective extension and outreach. Based on both our experiences and research and those of leading scholars and practitioners in the field, we offer the following ten ideas for thought, debate, discussion, and implementation. Effective extension systems must: be institutionalized, well-defined, and well-funded; address important/contemporary issues/problems; be sufficiently nimble and flexible in order to address emerging issues; be a credible and unbiased source for information/education and solutions/research; understand the needs of its customers; embrace participatory and integrated approaches; recognize that little happens in isolation and create regional/global sustainable partnership/linkages with governments, NGOs, researchers and educators; be excellent stewards of resources acquired; recognize that return on investment (ROI) from its research and outreach must be well-documented; and allow for decentralized decision-making and action when warranted

Effective information and the influence of an extension event on perceptions and adoption

Perceptions are known to play an important role in the innovation adoption decision. Once influential perceptions have been identified, there is the potential for information to influence adoption by changing these perceptions. In this paper, the influence of an extension workshop targeting grain growers’ perceptions known to be associated with the adoption of integrated weed management and herbicide resistance management has been measured using regression analysis. Consistent with a Bayesian learning framework, the greatest influence on grower perceptions and intended adoption behaviour was observed where information could be delivered with a high degree of certainty and validity.Crop Production/Industries, Farm Management,

Parafermionic conformal field theory on the lattice

Finding the precise correspondence between lattice operators and the continuum fields that describe their long-distance properties is a largely open problem for strongly interacting critical points. Here we solve this problem essentially completely in the case of the three-state Potts model, which exhibits a phase transition described by a strongly interacting 'parafermion' conformal field theory. Using symmetry arguments, insights from integrability, and extensive simulations, we construct lattice analogues of nearly all the relevant and marginal physical fields governing this transition. This construction includes chiral fields such as the parafermion. Along the way we also clarify the structure of operator product expansions between order and disorder fields, which we confirm numerically. Our results both suggest a systematic methodology for attacking non-free field theories on the lattice and find broader applications in the pursuit of exotic topologically ordered phases of matter.Comment: 27 pages, 4 figures; v2 added reference

Active Exploration for Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations. Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a generative model. However, these assumptions are too strong for many real-world applications, where the environment can be accessed only through sequential interaction. We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy. AceIRL uses previous observations to construct confidence intervals that capture plausible reward functions and find exploration policies that focus on the most informative regions of the environment. AceIRL is the first approach to active IRL with sample-complexity bounds that does not require a generative model of the environment. AceIRL matches the sample complexity of active IRL with a generative model in the worst case. Additionally, we establish a problem-dependent bound that relates the sample complexity of AceIRL to the suboptimality gap of a given IRL problem. We empirically evaluate AceIRL in simulations and find that it significantly outperforms more naive exploration strategies.Comment: Presented at Conference on Neural Information Processing Systems (NeurIPS), 202