Other by Sebastian Achter
Review of Artificial Societies and Social Simulation, 2019
The initiative presented below arose from a Lorentz Center workshop on Integrating Qualitative an... more The initiative presented below arose from a Lorentz Center workshop on Integrating Qualitative and Quantitative Evidence using Social Simulation (https://www.lorentzcenter.nl/lc/web/2019/1116/in- fo.php3?wsid=1116) (8-12 April 2019, Leiden, the Netherlands). At the beginning of this workshop, the attenders divided themselves into teams aiming to work on specific challenges within the broad domain of the workshop topic. Our team took up the challenge of looking at “Rigour, Transparency, and Reuse”. The aim that emerged from our initial discussions was to create a framework for augmenting rigour and transparency (RAT) of data use in ABM when both designing, analysing and publishing such models.
Papers by Sebastian Achter
Environmental Modelling and Software, Oct 31, 2023
Advancements in semiconductor industry have resulted in the need for extracting vital information... more Advancements in semiconductor industry have resulted in the need for extracting vital information from vast amount of data. In the operational process of supply chain, understanding customer demand data provides important insights for demand planning. Clustering analysis offers potential to identify latent information from multitudinous customer demand data and supports enhanced decision-making. In this paper, two clustering algorithms to identify customer demand patterns are presented, namely K-means and DBSCAN. The implementation of both algorithms on the prepared data sets is discussed and their performance is compared. The paper outlines the importance of deciphering valuable insights from customer demand data in the betterment of a distributed cognitive process of demand planning.
Winter Simulation Conference, Dec 3, 2017
Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on t... more Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on the demand side as well as a complex manufacturing process with non-deterministic failure modes on the production side. Understanding the complex interdependencies and processes of a SC is essential to realize opportunities and mitigate risks. However, this understanding is not easy to achieve due to the complexity of the processes and the non-deterministic human behavior determining SC planning performance. Our paper argues for an agent-based approach to understand and improve SC planning processes using an industry example. We give an overview of current work and elaborate on the need for integrating human behavior into the models. Overall, we conclude that agent-based simulation is a valuable method to identify favorable and unfavorable conditions for successful planning.
Atp-Edition, Aug 20, 2018
Advancements in semiconductor industry have resulted in the need for extracting vital information... more Advancements in semiconductor industry have resulted in the need for extracting vital information from vast amount of data. In the operational process of supply chain, understanding customer demand data provides important insights for demand planning. Clustering analysis offers potential to identify latent information from multitudinous customer demand data and supports enhanced decision- making. In this paper, two clustering algorithms to identify customer demand patterns are presented, namely K-means and DBSCAN. The implementation of both algorithms on the prepared data sets is discussed and their performance is compared. The paper outlines the importance of deciphering valuable insights from customer demand data in the betterment of a distributed cognitive process of demand planning.
Journal of Artificial Societies and Social Simulation, 2020
Using the agent-based model of Miller et al. (), which depicts how di erent types of individuals'... more Using the agent-based model of Miller et al. (), which depicts how di erent types of individuals' memory a ect the formation and performance of organizational routines, we show how a replicated simulation model can be used to develop theory. We also assess how standards, such as the ODD (Overview, Design concepts, and Details) protocol and DOE (design of experiments) principles, support the replication, evaluation, and further analysis of this model. Using the verified model, we conduct several simulation experiments as examples of di erent types of theory development. First, we show how previous theoretical insights can be generalized by investigating additional scenarios, such as mergers. Second, we show the potential of replicated simulation models for theory refinement, such as analyzing in-depth the relationship between memory functions and routine performance or routine adaptation.
Chemie Ingenieur Technik, 2015
Insights into accuracy of social scientists´forecasts of societal change., 2022
Abstract: How well can social scientists predict societal change, and what processes underlie the... more Abstract: How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments. Social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, teams were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data.
One-Sentence Summary: When forecasting societal change, social scientists were no better than the general public or naïve statistical benchmarks.
How well can social scientists predict societal change, and what processes underlie their predict... more How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate ...
winter simulation conference, Dec 3, 2017
International Journal of Social Research Methodology, Mar 30, 2022
2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), 2018
Advancements in semiconductor industry have resulted in the need for extracting vital information... more Advancements in semiconductor industry have resulted in the need for extracting vital information from vast amount of data. In the operational process of supply chain, understanding customer demand data provides important insights for demand planning. Clustering analysis offers potential to identify latent information from multitudinous customer demand data and supports enhanced decision-making. In this paper, two clustering algorithms to identify customer demand patterns are presented, namely K-means and DBSCAN. The implementation of both algorithms on the prepared data sets is discussed and their performance is compared. The paper outlines the importance of deciphering valuable insights from customer demand data in the betterment of a distributed cognitive process of demand planning.
While there is a number of frameworks and protocols in Agent-Based Modeling (ABM) that support th... more While there is a number of frameworks and protocols in Agent-Based Modeling (ABM) that support the documentation of different aspects of a simulation study, it is surprising to find only a small number dealing with the handling of data. Here we present the results of discussions we had on the topic at the Lorentz Center workshop on Integrating Qualitative and Quantitative Evidence using Social Simulation (8-12 April 2019, Leiden, the Netherlands). We believe that important distinctions to be considered in the context of data use documentation are the differences of data use in relation to modeling approaches (theory driven etc.) and data documentation needs at the different stages in the modeling process (conceptualization, specification, calibration, and validation). What we hope to achieve by presenting this paper at this conference, with the help of the community, is to move forward the development of a generally acceptable protocol for documenting data use in the ABM process.
Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on t... more Supply chain (SC) planning in the semiconductor industry is challenged by high uncertainties on the demand side as well as a complex manufacturing process with non-deterministic failure modes on the production side. Understanding the complex interdependencies and processes of a SC is essential to realize opportunities and mitigate risks. However, this understanding is not easy to achieve due to the complexity of the processes and the non-deterministic human behavior determining SC planning performance. Our paper argues for an agent-based approach to understand and improve SC planning processes using an industry example. We give an overview of current work and elaborate on the need for integrating human behavior into the models. Overall, we conclude that agent-based simulation is a valuable method to identify favorable and unfavorable conditions for successful planning.
Journal of Artificial Societies and Social Simulation
Using the agent-based model of Miller et al. (), which depicts how di erent types of individuals'... more Using the agent-based model of Miller et al. (), which depicts how di erent types of individuals' memory a ect the formation and performance of organizational routines, we show how a replicated simulation model can be used to develop theory. We also assess how standards, such as the ODD (Overview, Design concepts, and Details) protocol and DOE (design of experiments) principles, support the replication, evaluation, and further analysis of this model. Using the verified model, we conduct several simulation experiments as examples of di erent types of theory development. First, we show how previous theoretical insights can be generalized by investigating additional scenarios, such as mergers. Second, we show the potential of replicated simulation models for theory refinement, such as analyzing in-depth the relationship between memory functions and routine performance or routine adaptation.
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Other by Sebastian Achter
Papers by Sebastian Achter
One-Sentence Summary: When forecasting societal change, social scientists were no better than the general public or naïve statistical benchmarks.
One-Sentence Summary: When forecasting societal change, social scientists were no better than the general public or naïve statistical benchmarks.