Towards Leveraging the Frontline for Strategic Issue Identification under Uncertainty

This paper presents different corporate prediction aggregation techniques and introduces a new type of prediction mechanism linking the sensing of operational capabilities by frontline employees to the identification of fuzzy events and emerging strategic issues as ‘early warning signals’. Based on the literatures on prediction markets and fuzzy logic the methodology collects information from many diverse frontline employees to develop valid signaling predictors. Individuals in the frontline gain deep insights as they perform operational activities in direct interactions with many internal and external stakeholders and we tap into this unique knowledge source to identify new issues and opportunities for ongoing strategic decision-making. Aggregating dispersed information from crowds is not a new phenomenon. The capacity to aggregate heterogeneous and dispersed information from the environment is seen as a critical input for strategic decision making [Arrow, 1974; Hayek, 1945; Stinchcombe, 1990]. Hayek’s notion of information aggregation and dispersed knowledge, has established the foundations for prediction markets where the main objective of prediction markets is to create accurate predictions of given issues of interest, and such markets have demonstrated that crowds have the ability to predict outcomes [Berg, Forsythe and Rietz, 1996; 1997; Thompson, 2012: Wolfers and Zitzewitz, 2004]. Corporate prediction markets take various forms. Borrowing from the concepts used by Spann and Skiera (2003), they refer to the evolution in prediction aggregation as first-generation (G1) and second-generation (G2) prediction markets. In G1 markets participating employees invest in the outcome of already defined problems, such as, forecasts on next quarter’s sales volume, market entries by new competitors or performance of certain markets. In recent years, G2 markets, preference markets, aggregate predictions from the firm’s stakeholders about the probable success rates of various product concepts and ideas [Slamka, Jank and Skiera, 2012]. Hence, the participants in G1 and G2 prediction markets typically invest in the outcome of predefined time constrained issues. Here we propose a spring-off mechanism to G1 and G2 markets based on predictions without markets of fuzzy events or emerging issues not yet clearly defined, but nonetheless evolving phenomena to consider for responsive strategies. The notion of an event and its related probability constitute the most basic concepts of probability theory. An event is an accurately specified collection of points in a sample range. In contrast, in everyday life individuals often encounter situations in which an “event” is fuzzy and rather ill-defined than being a sharply defined collection of points [Zadeh, 1965]. We draw on fuzzy sets theory that offers mathematical models to deal with information that is uncertain and vague. That is, our contribution proposes formalized tools to deal with the intrinsic fuzziness in decision making problems [Fisher, 2003]

Fish Is Food - The FAO’s Fish Price Index

World food prices hit an all-time high in February 2011 and are still almost two and a half times those of 2000. Although three billion people worldwide use seafood as a key source of animal protein, the Food and Agriculture Organization (FAO) of the United Nations–which compiles prices for other major food categories–has not tracked seafood prices. We fill this gap by developing an index of global seafood prices that can help to understand food crises and may assist in averting them. The fish price index (FPI) relies on trade statistics because seafood is heavily traded internationally, exposing non-traded seafood to price competition from imports and exports. Easily updated trade data can thus proxy for domestic seafood prices that are difficult to observe in many regions and costly to update with global coverage. Calculations of the extent of price competition in different countries support the plausibility of reliance on trade data. Overall, the FPI shows less volatility and fewer price spikes than other food price indices including oils, cereals, and dairy. The FPI generally reflects seafood scarcity, but it can also be separated into indices by production technology, fish species, or region. Splitting FPI into capture fisheries and aquaculture suggests increased scarcity of capture fishery resources in recent years, but also growth in aquaculture that is keeping pace with demand. Regionally, seafood price volatility varies, and some prices are negatively correlated. These patterns hint that regional supply shocks are consequential for seafood prices in spite of the high degree of seafood tradability

Estimation of production risk and risk preference function:a nonparametric approach

While estimating parametric production models with risk, one faces two main problems. The first problem is associated with the choice of functional forms on the mean production function and the risk (variance) function. The second problem is associated with the specification of the risk preference function. In a parametric model the researcher chooses some ad hoc functional form on all these. It is obvious that the estimated (i) technology (mean production function), (ii) risk and (iii) risk preference functions are affected by the choice of functional form. In this paper we consider an estimation framework that avoids assuming parametric functions on all three. In particular, this paper deals with nonparametric estimation of the technology, risk and risk preferences of producers when they face uncertainty in production. Uncertainty is modeled in the context of production theory where producers’ maximize expected utility of anticipated profit. A multi-stage nonparametric estimation procedure is used to estimate the production function, the output risk function and the risk preference function. No distributional assumption is made on the random term representing production uncertainty. No functional form is assumed on the underlying utility function. Rice farming data from Philippines are used for an empirical application of the proposed model. Rice farmers are, in general, found to be risk averse; labor is risk decreasing while fertilizer, land and materials are risk increasing. The mean risk premium is about 3% of mean profit