The century-long tradition of reporting poll results as vote shares does not provide direct answers to the very questions voters are most interested in (e.g., who will govern). The present study provides experimental evidence showing that voters are largely unable to derive such information from polls themselves. When estimating the likelihood of two electoral outcomes, participants failed to realize that small changes in poll numbers can have large electoral consequences. The results suggest that it may be time to revisit how to communicate poll results to the public.
{"title":"Poll Illiteracy: How the Public Misunderstands Polls","authors":"A. Graefe","doi":"10.2139/ssrn.3834394","DOIUrl":"https://doi.org/10.2139/ssrn.3834394","url":null,"abstract":"The century-long tradition of reporting poll results as vote shares does not provide direct answers to the very questions voters are most interested in (e.g., who will govern). The present study provides experimental evidence showing that voters are largely unable to derive such information from polls themselves. When estimating the likelihood of two electoral outcomes, participants failed to realize that small changes in poll numbers can have large electoral consequences. The results suggest that it may be time to revisit how to communicate poll results to the public.","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"39 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125874207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher W. Karvetski, C. Meinel, D. Maxwell, Yunzi Lu, B. Mellers, P. Tetlock
Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. But these innovations have focused on easier-to quantify variables, like personnel selection, training, teaming, and crowd aggregation—and bypassed messier constructs, like qualitative properties of forecasters’ rationales. Here we adapt methods from natural language processing (NLP) and computational text analysis to identify distinctive reasoning strategies in the rationales of top forecasters, including: (a) cognitive styles, such as dialectical complexity, that gauge tolerance of clashing perspectives and efforts to blend them into coherent conclusions; (b) the use of comparison classes or base rates to inform forecasts; (c) metrics derived from the Linguistic Inquiry and Word Count (LIWC) program. Applying these tools to multiple forecasting tournaments and to forecasters of widely varying skill (from Mechanical Turkers to carefully culled “superforecasters”) revealed that: (a) top forecasters show higher dialectical complexity in their rationales, use more comparison classes, and offer more past-focused rationales; (b) experimental interventions, like training and teaming, that boost accuracy also influence NLP profiles of rationales, nudging them in a “superforecaster-like” direction.
{"title":"Forecasting the Accuracy of Forecasters from Properties of Forecasting Rationales","authors":"Christopher W. Karvetski, C. Meinel, D. Maxwell, Yunzi Lu, B. Mellers, P. Tetlock","doi":"10.2139/ssrn.3779404","DOIUrl":"https://doi.org/10.2139/ssrn.3779404","url":null,"abstract":"Geopolitical forecasting tournaments have stimulated the development of methods for improving probability judgments of real-world events. But these innovations have focused on easier-to quantify variables, like personnel selection, training, teaming, and crowd aggregation—and bypassed messier constructs, like qualitative properties of forecasters’ rationales. Here we adapt methods from natural language processing (NLP) and computational text analysis to identify distinctive reasoning strategies in the rationales of top forecasters, including: (a) cognitive styles, such as dialectical complexity, that gauge tolerance of clashing perspectives and efforts to blend them into coherent conclusions; (b) the use of comparison classes or base rates to inform forecasts; (c) metrics derived from the Linguistic Inquiry and Word Count (LIWC) program. Applying these tools to multiple forecasting tournaments and to forecasters of widely varying skill (from Mechanical Turkers to carefully culled “superforecasters”) revealed that: (a) top forecasters show higher dialectical complexity in their rationales, use more comparison classes, and offer more past-focused rationales; (b) experimental interventions, like training and teaming, that boost accuracy also influence NLP profiles of rationales, nudging them in a “superforecaster-like” direction.","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128629512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using the economic policy uncertainty (EPU) index of Baker et al. (2016), we examine the influence of EPU on the characteristics of USD/JPY exchange rate forecasts. Our sample period, which spans two decades, incorporates a range of economic and political conditions for the US and Japan. Consistent with higher EPU engendering a more complex information environment, our results clearly demonstrate that analyst forecast errors, and forecast dispersion, increase with EPU. US monetary policy uncertainty and Japanese trade policy uncertainty are particularly important in generating forecast dispersion. The empirical findings are consistent across forecast horizons ranging from 1-month to 1-year. This has important implications for market participants who use exchange rate forecasts when making business and investment decisions.
{"title":"The Influence of Policy Uncertainty on Exchange Rate Forecasting","authors":"L. Smales","doi":"10.2139/ssrn.3773975","DOIUrl":"https://doi.org/10.2139/ssrn.3773975","url":null,"abstract":"Using the economic policy uncertainty (EPU) index of Baker et al. (2016), we examine the influence of EPU on the characteristics of USD/JPY exchange rate forecasts. Our sample period, which spans two decades, incorporates a range of economic and political conditions for the US and Japan. Consistent with higher EPU engendering a more complex information environment, our results clearly demonstrate that analyst forecast errors, and forecast dispersion, increase with EPU. US monetary policy uncertainty and Japanese trade policy uncertainty are particularly important in generating forecast dispersion. The empirical findings are consistent across forecast horizons ranging from 1-month to 1-year. This has important implications for market participants who use exchange rate forecasts when making business and investment decisions.","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117265877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kainat Khowaja, Danial Saef, Sergej Sizov, W. Härdle
Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart of strategic management, yet not sufficiently motivated by data analytics driven controlling. One of the main obstacles in using e.g. count data time series for M&A seems to be the fact that the intensity of M&A is time varying at least in certain business sectors, e.g. communications. We propose a new automatic procedure to bridge this obstacle using novel statistical methods. The proposed approach allows for a selection of adaptive windows in count data sets by detecting significant changes in the intensity of events. We test the efficacy of the proposed method on a simulated count data set and put it into action on various M&A data sets. It is robust to aberrant behaviour and generates accurate forecasts for the evaluated business sectors. It also provides guidance for an a-priori selection of fixed windows for forecasting. Furthermore, it can be generalized to other business lines, e.g. for managing supply chains, sales forecasts, or call center arrivals, thus giving managers new ways for incorporating statistical modeling in strategic planning decisions.
{"title":"Data Analytics Driven Controlling: Bridging Statistical Modeling and Managerial Intuition","authors":"Kainat Khowaja, Danial Saef, Sergej Sizov, W. Härdle","doi":"10.2139/ssrn.3786669","DOIUrl":"https://doi.org/10.2139/ssrn.3786669","url":null,"abstract":"Strategic planning in a corporate environment is often based on experience and intuition, although internal data is usually available and can be a valuable source of information. Predicting merger & acquisition (M&A) events is at the heart of strategic management, yet not sufficiently motivated by data analytics driven controlling. One of the main obstacles in using e.g. count data time series for M&A seems to be the fact that the intensity of M&A is time varying at least in certain business sectors, e.g. communications. We propose a new automatic procedure to bridge this obstacle using novel statistical methods. The proposed approach allows for a selection of adaptive windows in count data sets by detecting significant changes in the intensity of events. We test the efficacy of the proposed method on a simulated count data set and put it into action on various M&A data sets. It is robust to aberrant behaviour and generates accurate forecasts for the evaluated business sectors. It also provides guidance for an a-priori selection of fixed windows for forecasting. Furthermore, it can be generalized to other business lines, e.g. for managing supply chains, sales forecasts, or call center arrivals, thus giving managers new ways for incorporating statistical modeling in strategic planning decisions.","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128974242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. A test is also introduced to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (US employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.
{"title":"Proper Scoring Rules for Evaluating Asymmetry in Density Forecasting","authors":"Matteo Iacopini, F. Ravazzolo, L. Rossini","doi":"10.2139/ssrn.3632278","DOIUrl":"https://doi.org/10.2139/ssrn.3632278","url":null,"abstract":"This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable's range. A test is also introduced to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (US employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132879405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stelios Arvanitis, S. Karabatı, T. Post, Valerio Potì
Abstract A nonparametric method for comparing multiple forecast models is developed and implemented. The hypothesis of Optimal Predictive Ability generalizes the Superior Predictive Ability hypothesis from a single given loss function to an entire class of loss functions. Distinction is drawn between General Loss functions, Convex Loss functions and Symmetric Convex Loss functions. The research hypothesis is formulated in terms of moment inequality conditions. The empirical moment conditions are reduced to an exact and finite system of linear inequalities based on piecewise-linear loss functions. The hypothesis can be tested in a statistically consistent way using a blockwise Empirical Likelihood Ratio test statistic. A computationally feasible test procedure computes the test statistic using Convex Optimization methods, and estimates conservative, data-dependent critical values using a majorizing chi-square limit distribution and a moment selection method. An empirical application to inflation forecasting reveals that a very large majority of thousands of forecast models are redundant, leaving predominantly Phillips Curve type models, when convexity and symmetry are assumed.
{"title":"Nonparametric Tests for Superior Predictive Ability","authors":"Stelios Arvanitis, S. Karabatı, T. Post, Valerio Potì","doi":"10.2139/ssrn.3251944","DOIUrl":"https://doi.org/10.2139/ssrn.3251944","url":null,"abstract":"Abstract A nonparametric method for comparing multiple forecast models is developed and implemented. The hypothesis of Optimal Predictive Ability generalizes the Superior Predictive Ability hypothesis from a single given loss function to an entire class of loss functions. Distinction is drawn between General Loss functions, Convex Loss functions and Symmetric Convex Loss functions. The research hypothesis is formulated in terms of moment inequality conditions. The empirical moment conditions are reduced to an exact and finite system of linear inequalities based on piecewise-linear loss functions. The hypothesis can be tested in a statistically consistent way using a blockwise Empirical Likelihood Ratio test statistic. A computationally feasible test procedure computes the test statistic using Convex Optimization methods, and estimates conservative, data-dependent critical values using a majorizing chi-square limit distribution and a moment selection method. An empirical application to inflation forecasting reveals that a very large majority of thousands of forecast models are redundant, leaving predominantly Phillips Curve type models, when convexity and symmetry are assumed.","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129863285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scenario planning also well known as scenario thinking or scenario analysis is a strategic planning tool that some organization use to create multiple long-term plans. The scenario planning is largely an adaptation and generalization of classic methods used by Military intelligence. The scenario planning method was that a group of analyst would generate imaginary simulation games for the decision makers. The scenarios contains known facts about the future such as demographics, geography, military, political, industrial information and mineral reserves with different alternatives social, technical, economic, environmental, educational, political and aesthetic trends which are the key driving forces. Scenario planning further may involvement of systems thinking, specifically the recognition of many factors which may combine in complex ways to create unprecedented future situation. The method also allow the inclusion of factors difficult to formalize like novel insights about the future, deep shifts in values, unexpected change in rules and regulations. Systems thinking used along with scenario planning leads to probable scenario story lines due to causal relationships can be demonstrated. When scenario planning is integrated with a system thinking approach to scenario development it is referred to as structural dynamics (Annon 2011a).
Royal Dutch Shell company has been using the Scenario planning for formulating their enterprise strategies for the last 3 decade actively to predict a highly uncertain industry future and stay ahead of other industry players.
In this Paper we will explore 3 different scenarios and strategic response for Royal Arcade Mall. The shopping mall industry has experienced vast expansion in UAE for the last one decade with at least 15 large scale shopping malls has been built from 2001 to 2011 and 5 malls still to come in the market within next 2 years period of time.
UAE is a popular destination and preferred work place for expatriate professionals and with this trend UAE expatriate population is going to increase in the coming years (anon-2007).
We will examine the Mall industry in Abu Dhabi in three different scenarios and will formulate strategic responses to these scenarios as follows:- a) No external environmental change b) Negative external change c) Positive external change
In the later stage of this Paper we will explore the relevance of Scenario planning in Strategic Management and decision making process.
{"title":"Scenario Planning: An Essential Tool for Handling the Organisational Crisis","authors":"M. Islam","doi":"10.2139/ssrn.3610398","DOIUrl":"https://doi.org/10.2139/ssrn.3610398","url":null,"abstract":"Scenario planning also well known as scenario thinking or scenario analysis is a strategic planning tool that some organization use to create multiple long-term plans. The scenario planning is largely an adaptation and generalization of classic methods used by Military intelligence. The scenario planning method was that a group of analyst would generate imaginary simulation games for the decision makers. The scenarios contains known facts about the future such as demographics, geography, military, political, industrial information and mineral reserves with different alternatives social, technical, economic, environmental, educational, political and aesthetic trends which are the key driving forces. Scenario planning further may involvement of systems thinking, specifically the recognition of many factors which may combine in complex ways to create unprecedented future situation. The method also allow the inclusion of factors difficult to formalize like novel insights about the future, deep shifts in values, unexpected change in rules and regulations. Systems thinking used along with scenario planning leads to probable scenario story lines due to causal relationships can be demonstrated. When scenario planning is integrated with a system thinking approach to scenario development it is referred to as structural dynamics (Annon 2011a).<br><br>Royal Dutch Shell company has been using the Scenario planning for formulating their enterprise strategies for the last 3 decade actively to predict a highly uncertain industry future and stay ahead of other industry players. <br><br>In this Paper we will explore 3 different scenarios and strategic response for Royal Arcade Mall. The shopping mall industry has experienced vast expansion in UAE for the last one decade with at least 15 large scale shopping malls has been built from 2001 to 2011 and 5 malls still to come in the market within next 2 years period of time. <br><br>UAE is a popular destination and preferred work place for expatriate professionals and with this trend UAE expatriate population is going to increase in the coming years (anon-2007).<br><br>We will examine the Mall industry in Abu Dhabi in three different scenarios and will formulate strategic responses to these scenarios as follows:-<br>a) No external environmental change<br>b) Negative external change<br>c) Positive external change <br><br>In the later stage of this Paper we will explore the relevance of Scenario planning in Strategic Management and decision making process.<br>","PeriodicalId":372343,"journal":{"name":"DecisionSciRN: Decision-Making & Forecasting (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133270184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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