An Interval Approach to Measuring Decision Maker Preferences

King, Robert P.; Robison, Lindon J.

doi:10.2307/1240542

Cited by 123 publications

(60 citation statements)

References 9 publications

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“…Developed by Meyer (1977), stochastic dominance with respect to a function (SDRF) ranks uncertain choices on the basis of the lower and upper bounds of decision makers' absolute risk aversion levels (Harris and Mapp, 1986;King and Robison, 1981). In other words, SDRF establishes necessary and sufficient conditions for the cumulative distribution function of F (y) to be preferred to the cumulative distribution function of G (y) by all individuals whose absolute risk aversion functions lie between lower r 1 (y) and upper bounds r 2 (y) (Harris and Mapp, 1986).…”

Section: Stochastic Dominancementioning

confidence: 99%

“…In other words, SDRF establishes necessary and sufficient conditions for the cumulative distribution function of F (y) to be preferred to the cumulative distribution function of G (y) by all individuals whose absolute risk aversion functions lie between lower r 1 (y) and upper bounds r 2 (y) (Harris and Mapp, 1986). Stochastic dominance with respect to a function has been implemented by many empirical studies (Barham et al, 2011;Cochran et al, 1985;Greene et al, 1985;Harris and Mapp, 1986;King and Robison, 1981;de la Llata et al, 1999;Musser et al, 1981;Parcell and Langemeier, 1997;Ritchie et al, 2004;Zacharias and Grube, 1984). It is a practical tool to help farmers better understand their risk preferences and choices under price, yield, or weather uncertainty (King and Robison, 1981).…”

Section: Stochastic Dominancementioning

confidence: 99%

“…Stochastic dominance with respect to a function has been implemented by many empirical studies (Barham et al, 2011;Cochran et al, 1985;Greene et al, 1985;Harris and Mapp, 1986;King and Robison, 1981;de la Llata et al, 1999;Musser et al, 1981;Parcell and Langemeier, 1997;Ritchie et al, 2004;Zacharias and Grube, 1984). It is a practical tool to help farmers better understand their risk preferences and choices under price, yield, or weather uncertainty (King and Robison, 1981). The major advantage of SDRF is that it imposes no restrictions on the width of the relevant absolute risk aversion interval (King and Robison, 1981).…”

Section: Stochastic Dominancementioning

confidence: 99%

“…It is a practical tool to help farmers better understand their risk preferences and choices under price, yield, or weather uncertainty (King and Robison, 1981). The major advantage of SDRF is that it imposes no restrictions on the width of the relevant absolute risk aversion interval (King and Robison, 1981). It allows the lower and upper bounds on absolute risk aversion interval to vary among studies (King and Robison, 1981).…”

Section: Stochastic Dominancementioning

confidence: 99%

“…The major advantage of SDRF is that it imposes no restrictions on the width of the relevant absolute risk aversion interval (King and Robison, 1981). It allows the lower and upper bounds on absolute risk aversion interval to vary among studies (King and Robison, 1981). First-and second-degree stochastic dominance can be viewed as special cases of SDRF (King and Robison, 1981).…”

Section: Stochastic Dominancementioning

confidence: 99%

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Risk Management Strategies using Precision Agriculture Technology to Manage Potato Late Blight

et al. 2017

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Precision agriculture has emerged as a revolutionary technology, which transforms farming related data into useful information for agricultural decision-making. Th is paper compares precision farming technology with calendar-based approach in scheduling fungicide applications to manage potato (Solanum tuberosum L.) late blight disease. Th ree fungicide scheduling strategies were evaluated: calendar-based strategy, BlightPro decision support system based strategy (DSS-based strategy), and unsprayed control. Using results from 14 yr of computer simulation experiments for 59 locations in the United States, we constructed distributions of net return to all costs excluding fungicide cost and application cost per 0.41 ha (net return per 0.41 ha) for the calendar-based and DSS-based strategies at each location. Th ese distributions were then compared using three risk management methods: stochastic dominance, stochastic dominance with respect to a function, and stochastic effi ciency with respect to a function. Th e DSS-based strategy was identifi ed as the most eff ective approach to manage late blight in terms of disease suppression, net return per 0.41 ha, and risk-adjusted net return. Results indicate that the DSS-based strategy is the preferred method to schedule fungicide applications. Under high disease pressure circumstances, the economic benefi ts to potato growers of adopting the precision agriculture technology ranged from US$30 to $573 per 0.41 ha. For risk neutral individuals, who are concerned about the diff erence between average net return per 0.41 ha, the benefi ts ranged from $30 to $305 per 0.41 ha. Except for growers raising the moderately resistant potato cultivars, more risk averse individuals tended to benefi t more from adopting the precision agriculture technology, with benefi ts ranging from $38 to $573 per 0.41 ha. Abbreviations: AUDPC, area under disease progress curve; CE, certainty equivalent; DSS, decision support system; FSD, fi rst-degree stochastic dominance; RP, risk premium; net return per 0.41 ha, net return to all costs excluding fungicide cost and application cost per 0.41 ha; SERF, stochastic effi ciency with respect to a function; SSD, second-degree stochastic dominance; SDRF, stochastic dominance with respect to a function. Core ideas• Th e benefi ts of adopting precision farming technology was investivated in scheduling fungicide applications to manage potato late blight.• Th e precision farming technology is the preferred method to schedule fungicide applications in terms of disease suppression, net return per 0.41 ha, and risk-adjusted net return.• Th e increased adoption of the precision farming technology would help manage late blight, limit potential crop losses, and improve net returns.

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Section: Stochastic Dominancementioning

confidence: 99%

Section: Stochastic Dominancementioning

confidence: 99%

Section: Stochastic Dominancementioning

confidence: 99%

Section: Stochastic Dominancementioning

confidence: 99%

Section: Stochastic Dominancementioning

confidence: 99%

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Risk Management Strategies using Precision Agriculture Technology to Manage Potato Late Blight

et al. 2017

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Combining price forecasting with hedging of hogs: An evaluation using alternative measures of risk

Holt

Brandt

1985

Journal of Futures Markets

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ncreased price variability in most agricultural commodity markets over the past I decade has exposed producers and processors to potentially large losses. Even though the agricultural environment has become more risky, livestock and crop produeers have shown great reluctance to use the futures market, in spite of substantial empirical evidence which suggests that price risk can be reduced through routine and selective hedging (e.g.,

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Price‐risk management with options: Optimal market positions and institutional value

Ladd¹,

Hanson²

1991

Journal of Futures Markets

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An Interval Approach to Measuring Decision Maker Preferences

Cited by 123 publications

References 9 publications

Risk Management Strategies using Precision Agriculture Technology to Manage Potato Late Blight

Risk Management Strategies using Precision Agriculture Technology to Manage Potato Late Blight

Combining price forecasting with hedging of hogs: An evaluation using alternative measures of risk

Price‐risk management with options: Optimal market positions and institutional value

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