Agricultural Research and Poverty Alleviation: Some International Perspectives

Invited paper for the John L. Dillon AO Commemorative Day on ‘Agricultural Research: Challenges and Economics in the New Millenium’ The University of New England, Armidale NSW Australia, September 20, 2002Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies,

Synthesis report of workshop on assessing the impact of policy-oriented social science research in Scheveningen, The Netherlands, November 12-13 2001

Economists have engaged for some time in developing methodologies for assessing the economic impact of agricultural research and in undertaking empirical studies to measure this impact. In recent years, they have documented more than 1,800 estimates of rates of return to agricultural research. Economists have paid little attention, however, to how to evaluate the impact of social science research. A symposium conducted by IFPRI in 1997 was one of the first attempts to address this knowledge gap. In November 2001, the Netherlands Ministry of Foreign Affairs and IFPRI brought together a group of researchers to follow up on the earlier symposium. Their conclusions fell into two broad categories: how to measure or value the economic impact of policy-oriented social science research and how to enhance the effectiveness of such research in policymaking environments. A number of lessons emerged from the workshop for donors, governments, and researchers about how to enhance the effectiveness of policy-oriented social science research....Because much remains to be learned about evaluating the impact of policy-oriented social science research, the workshop participants concluded that IFPRI should take the lead in developing a consortium to help improve interdisciplinary methods of assessing impact. The consortium would consist of institutions, donors, and individuals and would work in partnership with developing countries. Institutions could learn from each other about best practices and in the process exploit synergies, thereby increasing effectiveness and reducing the costs of what is an expensive undertaking.Agricultural research Economic aspects. ,Evaluation. ,Rate of return. ,Social sciences Research Evaluation. ,Research Economic aspects Congresses. ,

A Perspective on Unique Information: Directionality, Intuitions, and Secret Key Agreement

Recently, the partial information decomposition emerged as a promising framework for identifying the meaningful components of the information contained in a joint distribution. Its adoption and practical application, however, have been stymied by the lack of a generally-accepted method of quantifying its components. Here, we briefly discuss the bivariate (two-source) partial information decomposition and two implicitly directional interpretations used to intuitively motivate alternative component definitions. Drawing parallels with secret key agreement rates from information-theoretic cryptography, we demonstrate that these intuitions are mutually incompatible and suggest that this underlies the persistence of competing definitions and interpretations. Having highlighted this hitherto unacknowledged issue, we outline several possible solutions.Comment: 5 pages, 3 tables; http://csc.ucdavis.edu/~cmg/compmech/pubs/pid_intuition.ht

Unique Information via Dependency Constraints

The partial information decomposition (PID) is perhaps the leading proposal for resolving information shared between a set of sources and a target into redundant, synergistic, and unique constituents. Unfortunately, the PID framework has been hindered by a lack of a generally agreed-upon, multivariate method of quantifying the constituents. Here, we take a step toward rectifying this by developing a decomposition based on a new method that quantifies unique information. We first develop a broadly applicable method---the dependency decomposition---that delineates how statistical dependencies influence the structure of a joint distribution. The dependency decomposition then allows us to define a measure of the information about a target that can be uniquely attributed to a particular source as the least amount which the source-target statistical dependency can influence the information shared between the sources and the target. The result is the first measure that satisfies the core axioms of the PID framework while not satisfying the Blackwell relation, which depends on a particular interpretation of how the variables are related. This makes a key step forward to a practical PID.Comment: 15 pages, 7 figures, 2 tables, 3 appendices; http://csc.ucdavis.edu/~cmg/compmech/pubs/idep.ht

Unique Information and Secret Key Agreement

The partial information decomposition (PID) is a promising framework for decomposing a joint random variable into the amount of influence each source variable Xi has on a target variable Y, relative to the other sources. For two sources, influence breaks down into the information that both X0 and X1 redundantly share with Y, what X0 uniquely shares with Y, what X1 uniquely shares with Y, and finally what X0 and X1 synergistically share with Y. Unfortunately, considerable disagreement has arisen as to how these four components should be quantified. Drawing from cryptography, we consider the secret key agreement rate as an operational method of quantifying unique informations. Secret key agreement rate comes in several forms, depending upon which parties are permitted to communicate. We demonstrate that three of these four forms are inconsistent with the PID. The remaining form implies certain interpretations as to the PID's meaning---interpretations not present in PID's definition but that, we argue, need to be explicit. These reveal an inconsistency between third-order connected information, two-way secret key agreement rate, and synergy. Similar difficulties arise with a popular PID measure in light the results here as well as from a maximum entropy viewpoint. We close by reviewing the challenges facing the PID.Comment: 9 pages, 3 figures, 4 tables; http://csc.ucdavis.edu/~cmg/compmech/pubs/pid_skar.htm. arXiv admin note: text overlap with arXiv:1808.0860

Modes of Information Flow

Information flow between components of a system takes many forms and is key to understanding the organization and functioning of large-scale, complex systems. We demonstrate three modalities of information flow from time series X to time series Y. Intrinsic information flow exists when the past of X is individually predictive of the present of Y, independent of Y's past; this is most commonly considered information flow. Shared information flow exists when X's past is predictive of Y's present in the same manner as Y's past; this occurs due to synchronization or common driving, for example. Finally, synergistic information flow occurs when neither X's nor Y's pasts are predictive of Y's present on their own, but taken together they are. The two most broadly-employed information-theoretic methods of quantifying information flow---time-delayed mutual information and transfer entropy---are both sensitive to a pair of these modalities: time-delayed mutual information to both intrinsic and shared flow, and transfer entropy to both intrinsic and synergistic flow. To quantify each mode individually we introduce our cryptographic flow ansatz, positing that intrinsic flow is synonymous with secret key agreement between X and Y. Based on this, we employ an easily-computed secret-key-agreement bound---intrinsic mutual information&mdashto quantify the three flow modalities in a variety of systems including asymmetric flows and financial markets.Comment: 11 pages; 10 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/ite.ht

Limits on violations of Lorentz Symmetry from Gravity Probe B

Generic violations of Lorentz symmetry can be described by an effective field theory framework that contains both general relativity and the standard model of particle physics called the Standard-Model Extension (SME). We obtain new constraints on the gravitational sector of the SME using recently published final results from Gravity Probe B. These include for the first time an upper limit at the 10^(-3) level on the time-time component of the new tensor field responsible for inducing local Lorentz violation in the theory, and an independent limit at the 10^(-7) level on a combination of components of this tensor field.Comment: 8 pages, 1 figur