Stochastic efficiency analysis with risk aversion bounds: a simplified approach

Hardaker, J. Brian; Richardson, James W.; Lien, Gudbrand; Schumann, Keith D.

doi:10.1111/j.1467-8489.2004.00239.x

Cited by 230 publications

(314 citation statements)

References 15 publications

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“…In economic studies, the SSD rule has commonly been considered too coarse to be used effectively for practical purposes (Hardaker et al 2004, Hardaker andLien 2010). However, we found the discriminatory power of the SSD rule to be adequate for our geographical risk mapping case.…”

Section: Impact Of Adding the Notion Of Risk Aversionmentioning

confidence: 54%

“…Essentially, the concavity condition is a very basic definition of general risk-averse preferences (i.e., by eliminating the cases when the decisionmaker is risk-neutral or risk-seeking) and does not define a specific range of risk-averse preferences (such as moderate to extreme risk aversion). While it is possible to impose further restrictive assumptions on the type of risk-averse behaviour -for instance, by assuming a particular functional form of the EUF or limiting the degree of risk aversion to an upper and lower bounds (Meyer 1977, Meyer et al 2009, Hardaker et al 2004) -estimating the shape of the EUF in the invasive species management context could be problematic given the wide variety of pest invasion problems and the diverse spectrum of decision-making skills among pest management professionals.…”

Section: The Risk Aversion Conceptmentioning

confidence: 99%

“…For example, stochastic dominance with respect to a function (SDRF) limits the range of the absolute risk aversion measure to arbitrary chosen limits (Meyer 1977;Meyer et al 2009). Alternatively, stochastic efficiency with respect to a function (SERF) restricts the variation of the degree of risk aversion to an arbitrarily defined range but ranks risky alternatives in terms of their certainty equivalents (CE) (Hardaker et al 2004;Hardaker and Lien 2010). The SERF method requires making additional inferences about the functional form of the expected utility function and adds the restrictive assumption that the measure of risk aversion used is held constant as the level of outcomes changes (Hardaker and Lien 2010).…”

Section: Impact Of Adding the Notion Of Risk Aversionmentioning

confidence: 99%

“…Another measure, the stochastic efficiency with respect to a function, or SERF (Hardaker et al 2004;Hardaker and Lien 2010), ranks risky alternatives in terms of certainty equivalents (CE) while assuming that the degree of risk aversion varies within a defined range. The SERF method requires making additional assumptions about the functional form of a decision-maker's expected utility function and assumes that the decision-maker's risk aversion metric is of the same functional form as those lower and upper risk aversion bounds.…”

Section: Stochastic Dominance (Sd) Rulementioning

confidence: 99%

See 3 more Smart Citations

Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

Yemshanov¹,

Koch²,

Ducey³

et al. 2013

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Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker.We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first-and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. NeoBiotaDenys Yemshanov et al. / NeoBiota 18: 193-218 (2013) 194While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada.

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Section: Impact Of Adding the Notion Of Risk Aversionmentioning

confidence: 54%

Section: The Risk Aversion Conceptmentioning

confidence: 99%

Section: Impact Of Adding the Notion Of Risk Aversionmentioning

confidence: 99%

Section: Stochastic Dominance (Sd) Rulementioning

confidence: 99%

See 2 more Smart Citations

Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

Yemshanov¹,

Koch²,

Ducey³

et al. 2013

View full text Add to dashboard Cite

show abstract

“…That decision can be made by applying the stochastic efficiency with respect to a function (SERF) criterion [13] to the distributions of the estimated values of ENRT. The dominant or preferred FRT for each climate future with the SERF criterion is the one with the highest certainty equivalent [14], which is the payoff amount a manager is willing to receive in exchange for accepting the variability in ENRT for a particular FRT. For example, the SERF criterion has been applied assuming: (1) the manager's risk aversion coefficient (RAC) is in the range (0, 0.03), where 0 implies the manager is risk-neutral and RAC >0 implies the manager is risk-averse [15]; (2) constant absolute risk aversion (i.e., the risk premium a manager is willing to pay to reduce ENRT risk does not vary with the level of ENRT); and (3) the manager's utility function is exponential in ENRT (i.e., u[ENRT·I] = exp[−RAC × ENRT]) [16].…”

Section: Decision Model For Determining Preferred Frtsmentioning

confidence: 99%

Conceptual Framework for Assessing the Sustainability of Forest Fuel Reduction Treatments and Their Adaptation to Climate Change

Prato

2015

Sustainability

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Applying fuel reduction treatments (FRTs) to forested landscapes can alleviate undesirable changes in wildfire benefits and costs due to climate change. A conceptual framework was developed for determining the preferred FRTs across planning periods, adapting FRTs to future climate change, assessing the sustainability of adaptive responses to climate change, and evaluating the validity of the two premises motivating this issue of Sustainability. The conceptual framework: (1) accounts for uncertainty about future climate change and its effects on management objectives for FRTs; (2) employs biophysical simulation and mental models to estimate the management objectives for FRTs; (3) uses fuzzy TOPSIS to determine the preferred FRTs for climate futures; (4) employs the minimax regret criterion to identify the preferred FRT for each planning period; (5) determines the best strategy for adapting FRTs to future climate change; and (6) assesses landscape sustainability when using the preferred FRTs. The framework is demonstrated with constructed examples for adapting FRTs to climate change for privately-and publicly-owned forested landscapes. Based on the conceptual framework, current knowledge does not allow determining with certainty whether managers' adaptations of FRTs to future climate change are sustainable or unsustainable due to type I and II decision errors.

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Economic effect of fall vs. spring plowing of forage on following potato production in Prince Edward Island, Canada

Khakbazan

Nyiraneza

Jiang

et al. 2020

Agrosystems Geosci & Env

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Fall plowing of forage in a typical barley (Hordeum vulgare L.)–forage–potato (Solanum tuberosum L.) rotation in Prince Edward Island (PEI), Canada has led to negative environmental impacts, including soil erosion and nitrate leaching to groundwater. Data from five locations in PEI during 2009–2016 were assessed to determine the effects of delaying plow‐down of forage from early and late fall to spring on economic returns and risk of returns trade‐offs for potato producers. Factors related to fall or spring plowing such as soil erosion, nitrate leaching, planting date, effect on weeds, insects and diseases, potato harvest loss, and labor constraints were quantified. Potato yields were the same for fall and spring plowing; however, combined data for the five experiments showed late fall plowing was preferred over spring plowing for risk‐averse or neutral potato growers. Risk neutral farmers would require receiving between CAN$229 and $836 ha−1 yr−1, depending on yield loss for spring plowing due to delayed seeding, to be indifferent between fall and spring plowing options. Risk‐averse farmers at all levels of risk aversion would require being paid more than $600 ha−1 yr−1 to be indifferent between fall and spring tillage when 4–6% of yield loss for spring plowing due to delayed planting is assumed. Although spring tillage provides reductions in the risk of soil erosion and nitrate leaching, it also affects production risk and uncertainty. Therefore, we recommend farmers plow forage as late as possible in the autumn and replace it with other conservation tillage practices.

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Stochastic efficiency analysis with risk aversion bounds: a simplified approach

Cited by 230 publications

References 15 publications

Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker’s perspective

Conceptual Framework for Assessing the Sustainability of Forest Fuel Reduction Treatments and Their Adaptation to Climate Change

Economic effect of fall vs. spring plowing of forage on following potato production in Prince Edward Island, Canada

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