Optimizing information efficiency in grant markets

With governments cutting back on structural funding, grant-seeking organizations or individuals (e.g., in art, science, or philanthropy) increasingly depend on incidental and private grants to pursue their goals. Decentralized grant sources (grantors) are hard to locate and often introduce idiosyncratic guidelines for potential applicants. As a result, grant-seekers (grantees) spend increasing resources on reviewing grant opportunities at the cost of resources allocated to their societal goals. We frame this problem as one of market information inefficiency, and propose a semi-automated information mediation system to improve the exchange of information in grant markets.
The system (1) identifies grantors/grantees using existing sources and pre-defined terms guided online search, (2) Imports survey & web data of existing exchanges, (3) uses provenance information, evidential reasoning and semantic relatedness to model vocabulary-similarity based potential best fits, (4) uses survey-based information on pragmatics-based sign/word use underlying partner selection, and (5) estimates information exchange efficiency by comparing the data from (1) & (2) with (3) & (4).Whereas (1) and (2) assess the actual information exchange, (3) and (4) estimate optimized information exchange (including potential grantees’ applications’ success rate) by modeling dynamic behavior of competing grantees, grantors’ reputation and potential grantees’ perceived trustworthiness.
Communication science contributes by providing a network-based conceptualization of grant markets, semiotics to identify and distinguish these markets, word lists for Web extraction, search terms categories, historical data on application successes, survey data on grantee’s search behavior, and by validating the resulting system’s recommendations. Computer science contributes by implementing Web extraction techniques, providing measures for semantic relatedness, estimating findability per grantor, providing and implementing a matching algorithm, and modeling actors’ future behavior, reputation, expertise, and trustworthiness by use of provenance information.
The resulting system is a first step towards decreasing search costs for both grantors and grantees by enabling better targeting of grant applications.