Economics of Robust Surveillance on Exotic Animal Diseases: the Case of Bluetongue

Control of emerging animal diseases critically depends on their early detection. However, designing surveillance programs for exotic and emerging diseases is very challenging because of knowledge gaps on the probability of incursion and mechanisms of spread. Using the example of Bluetongue Virus, which is exotic to the UK, we develop a metapopulation epidemic-economic modelling framework that considers the incursion, detection, spread and control of a disease in a livestock production system composed of heterogeneous subpopulations. The model is then embedded in an information gap (info-gap) framework to assess the robustness of surveillance and vaccination policies to unacceptable outbreaks losses and applied to the case of Bluetongue in the UK. The results show that active reporting of suspect clinical signs by farmers is a very robust way to reduce unacceptable outcomes. Vaccination of animals in high risk regions led to robustly protective programs. If vaccines are not available, surveillance targeted to the high risk region is very robust even if the extent of the high risk region is not known and effectiveness of detection is very low. Surveillance programs focusing in all regions with the same intensity are in general not robust unless the dispersal of the vector connecting both regions is very high.compartmental epidemic model, emergent animal disease, Knightian uncertainty, sentinel surveillance system, Livestock Production/Industries,

Investment Model Uncertainty and Fair Pricing

Modern investment theory takes it for granted that a Security Market Line (SML) is as certain as its "corresponding" Capital Market Line. (CML). However, it can be easily demonstrated that this is not the case. Knightian non-probabilistic, information gap uncertainty exists in the security markets, as the bivariate "Galton's Error" and its concomitant information gap proves (Journal of Banking & Finance, 23, 1999, 1793-1829). In fact, an SML graph needs (at least) two parallel horizontal beta axes, implying that a particular mean security return corresponds with a limited Knightian uncertainty range of betas, although it does correspond with only one market portfolio risk volatility. This implies that a security' risk premium is uncertain and that a Knightian uncertainty range of SMLs and of fair pricing exists. This paper both updates the empirical evidence and graphically traces the financial market consequences of this model uncertainty for modern investment theory. First, any investment knowledge about the securities risk remains uncertain. Investment valuations carry with them epistemological ("modeling") risk in addition to the Markowitz-Sharpe market risk. Second, since idiosyncratic, or firm-specific, risk is limited-uncertain, the real option value of a firm is also limited-uncertain This explains the simultaneous coexistence of different analyst valuations of investment projects, particular firms or industries, included a category "undecided." Third, we can now distinguish between "buy", "sell" and "hold" trading orders based on an empirically determined collection of SMLs, based this Knightian modeling risk. The coexistence of such simultaneous value signals for the same security is necessary for the existence of a market for that security! Without epistemological investment uncertainty, no ongoing markets for securities could exist. In the absence of transaction costs and other inefficiencies, Knightian uncertainty is the necessary energy for market trading, since it creates potential or perceived arbitrage (= trading) opportunities, but it is also necessary for investors to hold securities. Knightian uncertainty provides a possible reason why the SEC can't obtain consensus on what constitutes "fair pricing." The paper also shows that Malkiel's recommended CML-based investments are extremely conservative and non-robust.capital market line, security market line, beta, investments, decision-making, Knightian uncertainty, robustness, information-gap, Galton's Error, real option value

Social Distance Into Factual Information Distance about COVID-19 in Indonesia Whatsapp Groups

WhatsApp has become a forum for communication and information related to the COVID-19 pandemic, so there are messages that ignore the identification and validation of news facts. This creates an information gap on WhatsApp. This study aims to analyze the form of information gaps related to information on the COVID-19 pandemic in Indonesia shared by WhatsApp group users. This research uses quantitative research methods, with data collection techniques through documentation of text messages and pictures, as well as the results of a survey conducted on WhatsApp group users. The results of this research indicate that the information gap in reporting the COVID-19 pandemic occurs due to information gaps in the form of uncertainty, strong beliefs and opportunities to choose information and decisions to choose information values. The substance of this research contributes in the form of new policy recommendations in assessing information gaps in social media by validating the truth of the facts in each message received and shared to another users

Information gap analysis of the Slovenian forest inventory data in the light of MCPFE requirements

V okviru evropskega procesa MCPFE se stanje gozdov in trajnost gospodarjenja znjimi preverja na podlagi stanja ter sprememb šestih Helsinških meril in podrejenih kazalnikov trajnostnega gospodarjenja. Opredelili smo razhajanja - informacijske vrzeli - med informacijskimi zahtevami količinskih kazalnikov MCPFE in razpoložljivimi podatki gozdne inventure. Vrzeli smo opredelili na podlagi sedmih kakovostnih meril. Največji informacijski vrzeli smo z vidika podajanja stanja kazalnikov opredelili v okviru 3., z vidika sprememb pa 4. Helsinškega merila. Glavna vzroka za to sta, da gozdna inventura ne daje nikakršnih podatkov za dva kazalnika 3. in en kazalnik 4. Helsinškega merila, ter dejstvo, da podatki velikokrat obstajajo le za eno obdobje. Vrzelim botrujejo tudi neusklajenost definicij, prostorska nepopolnost podatkov in nepreglednost metodologij. Za zmanjšanje informacijskih vrzeli bi bilo treba vsisteme zbiranja podatkov vpeljati nove znake (npr. gozdni tipi, enodobni/raznodobni sestoji, tipi obnove), postopki bi morali temeljiti na jasnih statističnih načelih, ki jih je treba dokumentirati.Within the framework of the European MCPFE process, the state of forests and forest management is assessed on the basis of the state and changes of six Helsinki criteria and subordinated sustainable management indicators. We have identified gaps - information gaps - between information requirements of quantitative MCPFE indicators and available forest inventory data. Gaps were identified according to seven quality criteria. The biggest information gaps were identified in the 3rd - state of indicators - and in the 4th Helsinki criteria - changes of indicators. The main reasons for this lie in the fact that forest inventory does not offer any data for two indicators of the 3rd and one indicator of the 4th Helsinki criteria, and the fact that in many cases data exist only for a single period. Definitions that are not harmonized, spatial incompleteness of data and lack of clarity in methodologies also contribute to these gaps. To reduce information gaps, new forest characteristic indicators should be introduced in data obtaining protocols (e.g. forest types, even-/uneven-aged stands, types of regeneration), processes should be based on clear statistical principles, which have to be documented

A resource allocation model for deep uncertainty (RAM-DU) with application to the Deepwater Horizon oil spill

Deep uncertainty usually refers to problems with epistemic uncertainty in which the analyst or decision maker has very little information about the system, data are severely lacking, and different mathematical models to describe the system may be possible. Since little information is available to forecast the future, selecting probability distributions to represent this uncertainty is very challenging. Traditional methods of decision making with uncertainty may not be appropriate for deep uncertainty problems. This paper introduces a novel approach to allocate resources within complex and very uncertain situations. The resource allocation model for deep uncertainty (RAM-DU) incorporates different types of uncertainty (e.g., parameter, structural, model uncertainty) and can consider every possible model, different probability distributions, and possible futures. Instead of identifying a single optimal alternative as in most resource allocation models, RAM-DU recommends an interval of allocation amounts. The RAM-DU solution generates an interval for one or multiple decision variables so that the decision maker can allocate any amount within that interval and still ensure that the objective function is within a predefined level of optimality for all the different parameters, models, and futures under consideration. RAM-DU is applied to allocating resources to prepare for and respond to a Deepwater Horizon-type oil spill. The application identifies allocation intervals for how much should be spent to prepare for this type of oil spill and how much should be spent to help industries recover from the spill

Future prospects for energy technologies: insights from expert elicitations

Expert elicitation is a structured approach for obtaining judgments from experts about items of interest to decision makers. This method has been increasingly applied in the energy domain to collect information on the future cost, technical performance, and associated uncertainty of specific energy technologies. This article has two main objectives: (1) to introduce the basics of expert elicitations, including their design and implementation, highlighting their advantages and disadvantages and their potential to inform policymaking and energy system decisions; and (2) to discuss and compare the results of a subset of the most recent expert elicitations on energy technologies, with a focus on future cost trajectories and implied cost reduction rates. We argue that the data on future energy costs provided by expert elicitations allows for more transparent and robust analyses that incorporate technical uncertainty, which can then be used to support the design and assessment of energy and climate change mitigation policies.V. Bosetti would like to acknowledge funding from the ERC (grant agreement 336703 – RISICO). L.D. Anadón, L. Aleluia Reis and E. Verdolini would like to acknowledge funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement 730403 – INNOPATHS)

Finding common ground when experts disagree: robust portfolio decision analysis

We address the problem of decision making under “deep uncertainty,” introducing an approach we call Robust Portfolio Decision Analysis. We introduce the idea of Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. We use this concept to derive a set of non-dominated portfolios; and then identify robust individual alternatives from the non-dominated portfolios. The Belief Dominance concept allows us to synthesize multiple conflicting sources of information by uncovering the range of alternatives that are intelligent responses to the range of beliefs. This goes beyond solutions that are optimal for any specific set of beliefs to uncover defensible solutions that may not otherwise be revealed. We illustrate our approach using a problem in the climate change and energy policy context: choosing among clean energy technology R&D portfolios. We demonstrate how the Belief Dominance concept can uncover portfolios that would otherwise remain hidden and identify robust individual investments