Steffen Q. Mueller | University of Hamburg (original) (raw)
Papers by Steffen Q. Mueller
Forecasting economic decisions under risk: The predictive importance of choice-process data, 2019
We investigate various statistical methods for forecasting risky choices and identify important d... more We investigate various statistical methods for forecasting risky choices and identify important decision predictors. Subjects (n=44) are presented a series of 50/50 gambles that each involves a potential gain and a potential loss, and subjects can choose to either accept or reject a displayed lottery. From this data, we use information on 8800 individual lottery gambles and specify four predictor-sets that include different combinations of input categories: lottery design, socioeconomic characteristics, past gambling behavior, eye-movements, and various psychophysiological measures that are recorded during the first three seconds of lottery-information processing. The results of our forecasting experiment show that choice-process data can effectively be used to forecast risky gambling decisions; however, we find large differences among models’ forecasting capabilities with respect to subjects,
predictor-sets, and lottery payoff structures.
Pre- and within-season attendance forecasting in Major League Baseball: A random forest approach
This study explores the forecasting of Major League Baseball game ticket sales and identifies imp... more This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sport demand, I consider differ-ent forecasting horizons and only use information that is publicly accessible in advance of a game or season. Models are trained using data from 2013 to 2014 to make predictions for the 2015 regular season. The static within-season approach is complemented by a dynamic month-ahead forecasting strategy. Out-of-sample performance is evaluated for individual teams and tested against least-squares regression and a naive lagged attendance forecast. My empirical results show high variation in team-specific prediction accuracy with respect to both models and forecasting horizons. Linear and tree-ensemble models, on average, do not vary substantially in predictive accuracy; however, OLS regression fails to account for various team-specific peculiarities.
We provide the first systematic documentation and analysis of a generation gap in direct democrac... more We provide the first systematic documentation and analysis of a generation gap in direct democracy outcomes across a wide range of topics using postelection survey data covering more than 300 Swiss referenda and four decades. We find that older voters are more likely to resist reform projects, particularly those that are associated with the political left. We separate age and cohort effects without imposing functional form constraints using a panel rank regression approach. The aging effect on political orientation is robust for controlling for arbitrary cohort effects and appears to be driven by expected utility maximization and not by habituation-induced status-quo bias. Our results suggest that population aging raises the hurdle for investment-like reform projects with positive net present values, long-run benefits and short-run costs in direct polls.
Drafts by Steffen Q. Mueller
Working Paper, 2020
We conduct a lottery experiment to assess the predictive importance of simple choice process metr... more We conduct a lottery experiment to assess the predictive importance of simple choice process metrics (SCPMs) in forecasting risky 50/50 gambling decisions using different types of machine learning algorithms
as well as traditional choice modeling approaches. The SCPMs are recorded during a fixed pre-decision phase and are derived from tracking subjects’ eye movements, pupil sizes, skin conductance, and cardiovascular and respiratory signals. Our study demonstrates that SCPMs provide relevant information for predicting gambling decisions, but we do not find forecasting accuracy to be substantially affected by adding SCPMs to standard choice data. Instead, our results show that forecasting accuracy highly depends on differences in subject-specific risk preferences and is largely driven by including information on lottery design variables. As a key result, we find evidence for dynamic changes in the predictive importance of psychophysiological responses that appear to be linked to habituation and resource-depletion effects. Subjects’ willingness to gamble and choice-revealing arousal signals both decrease as the experiment progresses. Moreover, our findings
highlight the importance of accounting for previous lottery payoff characteristics when investigating the role of emotions and cognitive bias in repeated decision-making scenarios.
Forecasting economic decisions under risk: The predictive importance of choice-process data, 2019
We investigate various statistical methods for forecasting risky choices and identify important d... more We investigate various statistical methods for forecasting risky choices and identify important decision predictors. Subjects (n=44) are presented a series of 50/50 gambles that each involves a potential gain and a potential loss, and subjects can choose to either accept or reject a displayed lottery. From this data, we use information on 8800 individual lottery gambles and specify four predictor-sets that include different combinations of input categories: lottery design, socioeconomic characteristics, past gambling behavior, eye-movements, and various psychophysiological measures that are recorded during the first three seconds of lottery-information processing. The results of our forecasting experiment show that choice-process data can effectively be used to forecast risky gambling decisions; however, we find large differences among models’ forecasting capabilities with respect to subjects,
predictor-sets, and lottery payoff structures.
Pre- and within-season attendance forecasting in Major League Baseball: A random forest approach
This study explores the forecasting of Major League Baseball game ticket sales and identifies imp... more This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sport demand, I consider differ-ent forecasting horizons and only use information that is publicly accessible in advance of a game or season. Models are trained using data from 2013 to 2014 to make predictions for the 2015 regular season. The static within-season approach is complemented by a dynamic month-ahead forecasting strategy. Out-of-sample performance is evaluated for individual teams and tested against least-squares regression and a naive lagged attendance forecast. My empirical results show high variation in team-specific prediction accuracy with respect to both models and forecasting horizons. Linear and tree-ensemble models, on average, do not vary substantially in predictive accuracy; however, OLS regression fails to account for various team-specific peculiarities.
We provide the first systematic documentation and analysis of a generation gap in direct democrac... more We provide the first systematic documentation and analysis of a generation gap in direct democracy outcomes across a wide range of topics using postelection survey data covering more than 300 Swiss referenda and four decades. We find that older voters are more likely to resist reform projects, particularly those that are associated with the political left. We separate age and cohort effects without imposing functional form constraints using a panel rank regression approach. The aging effect on political orientation is robust for controlling for arbitrary cohort effects and appears to be driven by expected utility maximization and not by habituation-induced status-quo bias. Our results suggest that population aging raises the hurdle for investment-like reform projects with positive net present values, long-run benefits and short-run costs in direct polls.
Working Paper, 2020
We conduct a lottery experiment to assess the predictive importance of simple choice process metr... more We conduct a lottery experiment to assess the predictive importance of simple choice process metrics (SCPMs) in forecasting risky 50/50 gambling decisions using different types of machine learning algorithms
as well as traditional choice modeling approaches. The SCPMs are recorded during a fixed pre-decision phase and are derived from tracking subjects’ eye movements, pupil sizes, skin conductance, and cardiovascular and respiratory signals. Our study demonstrates that SCPMs provide relevant information for predicting gambling decisions, but we do not find forecasting accuracy to be substantially affected by adding SCPMs to standard choice data. Instead, our results show that forecasting accuracy highly depends on differences in subject-specific risk preferences and is largely driven by including information on lottery design variables. As a key result, we find evidence for dynamic changes in the predictive importance of psychophysiological responses that appear to be linked to habituation and resource-depletion effects. Subjects’ willingness to gamble and choice-revealing arousal signals both decrease as the experiment progresses. Moreover, our findings
highlight the importance of accounting for previous lottery payoff characteristics when investigating the role of emotions and cognitive bias in repeated decision-making scenarios.