Steering Capital: Optimizing Financial Support for Innovation in Public Education

Examines efforts to align capital to education innovation and calls for clarity and agreement on problems, goals, and metrics; an effective R&D system; an evidence-based culture of continuous improvement; and transparent, comparable, and useful data

Supporting and Scaling Change: Lessons From the First Round of the Investing in Innovation (i3) Program

Assesses the degree to which the i3 program helped advance innovation in public education. Outlines takeaways, challenges, and recommendations for the Education Department and grantmakers, including optimizing support for different stages of innovation

Pull and Push: Strengthening Demand for Innovation in Education

Examines policy, information, and cultural barriers that minimize the "demand pull" for educational innovation. Calls for encouraging early adopters, bolstering smart adoption, providing better information, and rewarding productivity improvements

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the "quintessential" observations that best represent clusters in a dataset, by performing joint inference on cluster labels, prototypes and important features. Simultaneously, BCM pursues sparsity by learning subspaces, the sets of features that play important roles in the characterization of the prototypes. The prototype and subspace representation provides quantitative benefits in interpretability while preserving classification accuracy. Human subject experiments verify statistically significant improvements to participants' understanding when using explanations produced by BCM, compared to those given by prior art.Comment: Published in Neural Information Processing Systems (NIPS) 2014, Neural Information Processing Systems (NIPS) 201

Encouraging Social Innovation Through Capital: Using Technology to Address Barriers

Outlines how technology can help foster a robust capital market for public education innovation by improving content, linking technology with face-to-face networks, and streamlining transactions. Suggests steps for government, foundations, and developers

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13

A Structural VAR Approach to Estimating Budget Balance Targets

The Fiscal Responsibility Act 1994 states that, as a principle of responsible fiscal management, a New Zealand government should ensure total Crown debt is at a prudent level by ensuring total operating expenses do not exceed total operating revenues. In this paper a structural VAR model is estimated to evaluate the impact on the government's cash operating surplus (or budget balance) of four independent disturbances: supply, fiscal, real private demand, and nominal disturbances. Based on the distribution of these disturbances, stochastic simulations are undertaken to derive the level of the ex ante cash budget balance needed to achieve an actual cash budget balance, at a given level of probability, at some future time horizon.Budget target; Fiscal policy; Fiscal Responsibility Act; Structural VAR; Stochastic Simulation

Mediation and Negotiation in South Korea and Japan

Since Japan’s colonization of Korea from 1910 to 1945, relations between the two countries have been rocky.[1] In 1965, the United States helped with the signing of a normalization treaty between Japan and South Korea; Japan believes that this resolved all reparation questions.[2] However, the treaty was signed secretly and pushed through the legislature under South Korean dictator Park Chung-Hee. Additionally, Japan possessed much greater power than South Korea at the time, leaving Koreans with few options and raising questions from Koreans’ perspective of the treaty’s legitimacy.[3] More importantly, the treaty did not properly address the issues of Japanese wartime military brothels and forced labor,[4] nor did it clearly state “if the settlements were grant aid from Japan or reparations for colonization.”[5] As such, victims were not compensated, and capital secured from Japan through the treaty was used for economic development.[6] This post was originally published on the Cardozo Journal of Conflict Resolution website on October 22, 2021. The original post can be accessed via the Archived Link button above