This case explores the origins and current practices of DBL, a San Francisco-based venture capital fund and one of the first impact investment funds to achieve significant financial returns to scale. This case allows for a competitive analysis of DBL's investment strategy as it seeks to deploy $400m, as well as the opportunity to evaluate a specific investment in a solar power company targeting low-income consumers in Tanzania.

Pi was a large family office pioneering the concept of 100% portfolio impact investing. Tasked with preserving capital, generating moderate returns and advancing the family's social justice goals - Pi's Managing Directors had to identify appropriate products across asset classes. In this case, students will be required to assess an investment in HCAP Partners Fund III from the perspective of Pi and whether such an investment meets the family's core criteria.

Industry self-regulation has, in general, a lousy track record. Many studies have shown that it is often ineffective unless backed by the power of the state, and that in some cases it serves rather to forestall government intervention or to reduce competition than as genuine self-regulation. Many observers doubt that accelerated private sector regulation could really make a difference against the major public goods issues of our time. Yet we live in a time when many public goods issues are global and global governance mechanisms are at the very earliest stage of development, and in industry after industry leading firms are banding together in an attempt to regulate conduct-designing metrics, relying on independent auditors, and attempting to enforce compliance. Is this a plausible path forward? This note summarizes work in history, political science, and economics-drawing particularly on the work of Eleanor Ostrom-to explore this issue in depth.

For decades, computer scientists have tried to teach computers to think like human experts. Until recently, most of those efforts have failed to come close to generating the creative insights and solutions that seem to come naturally to the best researchers, doctors, and engineers. But now, Tony Hey, a VP of Microsoft Research, says we're witnessing the dawn of a new generation of powerful computer tools that can "mash up" vast quantities of data from many sources, analyze them, and help produce revolutionary scientific discoveries. Hey and his colleagues call this new method of scientific exploration "machine learning." At Microsoft, a team has already used it to innovate a method of predicting with impressive accuracy whether a patient with congestive heart failure who is released from the hospital will be readmitted within 30 days. It was developed by directing a computer program to pore through hundreds of thousands of data points on 300,000 patients and "learn" the profiles of patients most likely to be rehospitalized. The economic impact of this prediction tool could be huge: If a hospital understands the likelihood that a patient will "bounce back," it can design programs to keep him stable and save thousands of dollars in health care costs. Similar efforts to uncover important correlations that could lead to scientific breakthroughs are under way in oceanography, conservation, and AIDS research. And in business, deep data exploration has the potential to unearth critical insights about customers, supply chains, advertising effectiveness, and more.