[{"conference":{"name":"Hawaii International Conference on System Sciences"},"main_file_link":[{"open_access":"1","url":"https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f"}],"title":"Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time","author":[{"first_name":"Dirk","id":"51271","full_name":"Leffrang, Dirk","orcid":"0000-0001-9004-2391","last_name":"Leffrang"},{"first_name":"Kevin","last_name":"Bösch","full_name":"Bösch, Kevin"},{"last_name":"Müller","id":"72849","full_name":"Müller, Oliver","first_name":"Oliver"}],"date_created":"2023-01-18T10:53:51Z","oa":"1","date_updated":"2024-01-10T09:52:59Z","citation":{"short":"D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on System Sciences, 2023.","bibtex":"@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii International Conference on System Sciences}, author={Leffrang, Dirk and Bösch, Kevin and Müller, Oliver}, year={2023} }","mla":"Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” <i>Hawaii International Conference on System Sciences</i>, 2023.","apa":"Leffrang, D., Bösch, K., &#38; Müller, O. (2023). Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. <i>Hawaii International Conference on System Sciences</i>. Hawaii International Conference on System Sciences.","chicago":"Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In <i>Hawaii International Conference on System Sciences</i>, 2023.","ieee":"D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii International Conference on System Sciences, 2023.","ama":"Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time. In: <i>Hawaii International Conference on System Sciences</i>. ; 2023."},"year":"2023","language":[{"iso":"eng"}],"keyword":["Algorithm aversion","Time series","Decision making","Advice taking","Forecasting"],"department":[{"_id":"196"}],"user_id":"51271","_id":"37312","status":"public","abstract":[{"text":"Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners.","lang":"eng"}],"publication":"Hawaii International Conference on System Sciences","type":"conference"},{"title":"Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall","doi":"10.21314/JOR.2022.044","date_updated":"2024-04-17T13:34:54Z","date_created":"2022-01-13T11:23:02Z","author":[{"id":"23991","full_name":"Letmathe, Sebastian","last_name":"Letmathe","first_name":"Sebastian"},{"id":"20760","full_name":"Feng, Yuanhua","last_name":"Feng","first_name":"Yuanhua"},{"first_name":"André","orcid":"https://orcid.org/0000-0002-8058-8857","last_name":"Uhde","id":"36049","full_name":"Uhde, André"}],"year":"2022","jel":["C14","C51","C52","G17","G32"],"citation":{"apa":"Letmathe, S., Feng, Y., &#38; Uhde, A. (n.d.). Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>. <a href=\"https://doi.org/10.21314/JOR.2022.044\">https://doi.org/10.21314/JOR.2022.044</a>","bibtex":"@article{Letmathe_Feng_Uhde, title={Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall}, DOI={<a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>}, journal={Journal of Risk}, author={Letmathe, Sebastian and Feng, Yuanhua and Uhde, André} }","mla":"Letmathe, Sebastian, et al. “Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i>, doi:<a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>.","short":"S. Letmathe, Y. Feng, A. Uhde, Journal of Risk (n.d.).","ama":"Letmathe S, Feng Y, Uhde A. Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall. <i>Journal of Risk</i>. doi:<a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>","ieee":"S. Letmathe, Y. Feng, and A. Uhde, “Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall,” <i>Journal of Risk</i>, doi: <a href=\"https://doi.org/10.21314/JOR.2022.044\">10.21314/JOR.2022.044</a>.","chicago":"Letmathe, Sebastian, Yuanhua Feng, and André Uhde. “Semiparametric GARCH Models with Long Memory Applied to Value at Risk and Expected Shortfall.” <i>Journal of Risk</i>, n.d. <a href=\"https://doi.org/10.21314/JOR.2022.044\">https://doi.org/10.21314/JOR.2022.044</a>."},"publication_status":"inpress","keyword":["Semiparametric","long memory","GARCH models","forecasting","Value at Risk","Expected Shortfall","traffic light test","Basel Committee on Banking Supervision"],"language":[{"iso":"eng"}],"_id":"29317","user_id":"36049","department":[{"_id":"186"},{"_id":"19"}],"abstract":[{"text":"In this paper new semiparametric GARCH models with long memory are in- troduced. The estimation of the nonparametric scale function is carried out by an adapted version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised recommendations by the Basel Committee to measure market risk in the banks' trading books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH models are applied to obtain rolling one-step ahead forecasts for the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard regulatory traffic light tests (Basel Committee on Banking Supervision, 1996) and a newly introduced traffic light test for the ES are carried out for all models. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long memory GARCH models are an attractive alternative to their conventional, parametric counterparts.","lang":"eng"}],"status":"public","type":"journal_article","publication":"Journal of Risk"},{"department":[{"_id":"151"}],"user_id":"9557","_id":"17810","language":[{"iso":"eng"}],"keyword":["PHM 2019","crack propagation","forecasting","unevenly spaced time series","step ahead prediction","short time series"],"publication":"PHM Society European Conference","type":"conference","status":"public","abstract":[{"text":"In all fields, the significance of a reliable and accurate predictive model is almost unquantifiable. With deep domain knowledge, models derived from first principles typically outperforms other models in terms of reliability and accuracy. When it may become a cumbersome or an unachievable task to build or validate such models of complex (non-linear) systems, machine learning techniques are employed to build predictive models. However, the accuracy of such techniques is not only dependent on the hyper-parameters of the chosen algorithm, but also on the amount and quality of data. This paper investigates the application of classical time series forecasting approaches for the reliable prognostics of technical systems, where black box machine learning techniques might not successfully be employed given insufficient amount of data and where first principles models are infeasible due to lack of domain specific data. Forecasting by analogy, forecasting by analytical function fitting, an exponential smoothing forecasting method and the long short-term memory (LSTM) are evaluated and compared against the ground truth data. As a case study, the methods are applied to predict future crack lengths of riveted aluminium plates under cyclic loading. The performance of the predictive models is evaluated based on error metrics leading to a proposal of when to apply which forecasting approach.","lang":"eng"}],"volume":5,"author":[{"last_name":"Aimiyekagbon","id":"9557","full_name":"Aimiyekagbon, Osarenren Kennedy","first_name":"Osarenren Kennedy"},{"last_name":"Bender","full_name":"Bender, Amelie","id":"54290","first_name":"Amelie"},{"first_name":"Walter","full_name":"Sextro, Walter","id":"21220","last_name":"Sextro"}],"date_created":"2020-08-11T13:32:40Z","date_updated":"2023-09-22T09:13:16Z","title":"Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data","issue":"1","quality_controlled":"1","intvolume":"         5","citation":{"apa":"Aimiyekagbon, O. K., Bender, A., &#38; Sextro, W. (2020). Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. <i>PHM Society European Conference</i>, <i>5</i>(1).","short":"O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference, 2020.","bibtex":"@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}, year={2020} }","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” <i>PHM Society European Conference</i>, vol. 5, no. 1, 2020.","ama":"Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data. In: <i>PHM Society European Conference</i>. Vol 5. ; 2020.","chicago":"Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation of Time Series Forecasting Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates given Insufficient Data.” In <i>PHM Society European Conference</i>, Vol. 5, 2020.","ieee":"O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting approaches for the reliable crack length prediction of riveted aluminium plates given insufficient data,” in <i>PHM Society European Conference</i>, 2020, vol. 5, no. 1."},"year":"2020"},{"title":"Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors","doi":"10.2139/ssrn.1714399","main_file_link":[{"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1714399"}],"date_updated":"2022-01-06T06:54:41Z","author":[{"first_name":"Sönke","full_name":"Sievers, Sönke","id":"46447","last_name":"Sievers"},{"full_name":"Mokwa, Christopher Frederik","last_name":"Mokwa","first_name":"Christopher Frederik"}],"date_created":"2021-01-05T11:52:51Z","year":"2012","page":"42","jel":["G24","G32","M13","M41"],"citation":{"apa":"Sievers, S., &#38; Mokwa, C. F. (2012). <i>Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors</i>. <a href=\"https://doi.org/10.2139/ssrn.1714399\">https://doi.org/10.2139/ssrn.1714399</a>","mla":"Sievers, Sönke, and Christopher Frederik Mokwa. <i>Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors</i>. 2012, doi:<a href=\"https://doi.org/10.2139/ssrn.1714399\">10.2139/ssrn.1714399</a>.","bibtex":"@book{Sievers_Mokwa_2012, title={Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors}, DOI={<a href=\"https://doi.org/10.2139/ssrn.1714399\">10.2139/ssrn.1714399</a>}, author={Sievers, Sönke and Mokwa, Christopher Frederik}, year={2012} }","short":"S. Sievers, C.F. Mokwa, Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors, 2012.","ama":"Sievers S, Mokwa CF. <i>Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors</i>.; 2012. doi:<a href=\"https://doi.org/10.2139/ssrn.1714399\">10.2139/ssrn.1714399</a>","chicago":"Sievers, Sönke, and Christopher Frederik Mokwa. <i>Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors</i>, 2012. <a href=\"https://doi.org/10.2139/ssrn.1714399\">https://doi.org/10.2139/ssrn.1714399</a>.","ieee":"S. Sievers and C. F. Mokwa, <i>Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors</i>. 2012."},"publication_status":"published","keyword":["Management forecasts","Forecasting biases","Venture-backed start-ups","Projection methods"],"extern":"1","language":[{"iso":"eng"}],"_id":"20869","user_id":"46447","abstract":[{"lang":"eng","text":"This study provides evidence of significant biases in multi-year management forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany. We find that revenues and expenses are highly overestimated in each of the investigated one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate one-year-ahead profit forecasts but clearly overestimate their profit forecasts for all longer-term forecast horizons. Additional analyses reveal that teams with prior management experience issue even more overestimated forecasts and misrepresent their forward-looking information. In contrast, greater asset verifiability and corporate lead investors are associated with lower levels of forecast errors. All key results hold if bias is either measured by traditionally comparing forecasts to ex-post realizations or by using a cross-sectional projection approach based on historical accounting data developed by prior research."}],"status":"public","type":"working_paper"},{"_id":"5198","department":[{"_id":"275"}],"user_id":"64756","keyword":["Management forecasts","Forecasting biases","Venture-backed start-ups","Projection methods"],"language":[{"iso":"eng"}],"publication":"SSRN Electronic Journal","type":"journal_article","abstract":[{"lang":"eng","text":"This study provides evidence of significant biases in multi-year management forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany. We find that revenues and expenses are highly overestimated in each of the investigated one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate one-year-ahead profit forecasts but clearly overestimate their profit forecasts for all longer-term forecast horizons. Additional analyses reveal that teams with prior management experience issue even more overestimated forecasts and misrepresent their forward-looking information. In contrast, greater asset verifiability and corporate lead investors are associated with lower levels of forecast errors. All key results hold if bias is either measured by traditionally comparing forecasts to ex-post realizations or by using a cross-sectional projection approach based on historical accounting data developed by prior research. "}],"status":"public","date_updated":"2022-01-06T07:01:43Z","date_created":"2018-10-31T12:16:45Z","author":[{"first_name":"Christopher","full_name":"Mokwa, Christopher","last_name":"Mokwa"},{"first_name":"Sönke","last_name":"Sievers","full_name":"Sievers, Sönke"}],"title":"Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors","doi":"10.2139/ssrn.1714399","publication_status":"published","year":"2012","jel":["G24","G32","M13","M41"],"citation":{"mla":"Mokwa, Christopher, and Sönke Sievers. “Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors.” <i>SSRN Electronic Journal</i>, 2012, doi:<a href=\"https://doi.org/10.2139/ssrn.1714399\">10.2139/ssrn.1714399</a>.","bibtex":"@article{Mokwa_Sievers_2012, title={Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors}, DOI={<a href=\"https://doi.org/10.2139/ssrn.1714399\">10.2139/ssrn.1714399</a>}, journal={SSRN Electronic Journal}, author={Mokwa, Christopher and Sievers, Sönke}, year={2012} }","short":"C. Mokwa, S. Sievers, SSRN Electronic Journal (2012).","apa":"Mokwa, C., &#38; Sievers, S. (2012). Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors. <i>SSRN Electronic Journal</i>. <a href=\"https://doi.org/10.2139/ssrn.1714399\">https://doi.org/10.2139/ssrn.1714399</a>","chicago":"Mokwa, Christopher, and Sönke Sievers. “Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors.” <i>SSRN Electronic Journal</i>, 2012. <a href=\"https://doi.org/10.2139/ssrn.1714399\">https://doi.org/10.2139/ssrn.1714399</a>.","ieee":"C. Mokwa and S. Sievers, “Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors,” <i>SSRN Electronic Journal</i>, 2012.","ama":"Mokwa C, Sievers S. Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors. <i>SSRN Electronic Journal</i>. 2012. doi:<a href=\"https://doi.org/10.2139/ssrn.1714399\">10.2139/ssrn.1714399</a>"}}]