nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒12‒12
twenty papers chosen by
Stan Miles
Thompson Rivers University

  1. Application of machine learning in quantitative investment strategies on global stock markets By Jan Grudniewicz; Robert Ślepaczuk
  2. The impact of moving expenses on social segregation: a simulation with RL and ABM By Xinyu Li
  3. Predicting football outcomes from Spanish league using machine learning models By Michał Lewandowski; Marcin Chlebus
  4. Market risk assessment: A multi-asset, agent-based approach applied to the 0VIX lending protocol By Amit Chaudhary; Daniele Pinna
  5. Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines By Südbeck, Insa; Mindlina, Julia; Schnabel, André; Helber, Stefan
  6. Forecasting the Stability and Growth Pact compliance using Machine Learning By Kea Baret; Amelie Barbier-Gauchard; Theophilos Papadimitriou
  7. DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift By Defu Cao; Yousef El-Laham; Loc Trinh; Svitlana Vyetrenko; Yan Liu
  8. Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction By Thanh Trung Huynh; Minh Hieu Nguyen; Thanh Tam Nguyen; Phi Le Nguyen; Matthias Weidlich; Quoc Viet Hung Nguyen; Karl Aberer
  9. HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction By Youru Li; Zhenfeng Zhu; Xiaobo Guo; Shaoshuai Li; Yuchen Yang; Yao Zhao
  10. Deep learning and American options via free boundary framework By Chinonso Nwankwo; Nneka Umeorah; Tony Ware; Weizhong Dai
  11. Underemployment in a Computable General Equilibrium Model By Roberto Roson
  12. Predicting Household Resilience Before and During Pandemic with Classifier Algorithms By Surjaningsih, Ndari; Werdaningtyas, Hesti; Rahman, Faizal; Falaqh, Romadhon
  13. Deep Signature Algorithm for Path-Dependent American option pricing By Erhan Bayraktar; Qi Feng; Zhaoyu Zhang
  14. Using the web to predict regional trade flows: data extraction, modelling, and validation By Tranos, Emmanouil; Incera, Andre Carrascal; Willis, George
  15. FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning By Xiao-Yang Liu; Ziyi Xia; Jingyang Rui; Jiechao Gao; Hongyang Yang; Ming Zhu; Christina Dan Wang; Zhaoran Wang; Jian Guo
  16. Trade-related effects of Brexit. Implications for Central and Eastern Europe By Jan Hagemejer; Maria Dunin-Wąsowicz; Jan Jakub Michałek; Jacek Szyszka
  17. Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning By Dylan Radovic; Lucas Kruitwagen; Christian Schroeder de Witt; Ben Caldecott; Shane Tomlinson; Mark Workman
  18. AI, Skill, and Productivity: The Case of Taxi Drivers By Kanazawa, Kyogo; Kawaguchi, Daiji; Shigeoka, Hitoshi; Watanabe, Yasutora
  19. Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives By Zequn Jin; Lihua Lin; Zhengyu Zhang
  20. Open Science for Computer Simulation By Monks, Thomas; Harper, Alison; Anagnostou, Anastasia; Taylor, Simon J.E.

  1. By: Jan Grudniewicz (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)
    Abstract: The thesis undertakes the subject of machine learning based quantitative investment strategies. Several technical analysis indicators were employed as inputs to machine learning models such as Neural Networks, K Nearest Neighbor, Regression Trees, Random Forests, Naïve Bayes classifiers, Bayesian Generalized Linear Models and Support Vector Machines. Models were used to generate trading signals on WIG20, DAX, S&P500 and selected CEE indices in the period between 2002-01-01 to 2020-10-30. Strategies were compared with each other and with the benchmark buy-and-hold strategy in terms of achieved levels of risk and return. Quality of estimation was evaluated on independent subsets and with the use of sensitivity analysis. The research results indicated that quantitative strategies generate better risk adjusted returns than passive strategies and that for the analysed indices predominantly Bayesian Generalized Linear Model and Naïve Bayes were the best performing models. More comprehensive rank approach based on the results for all analysed models and indices allowed to select Bayesian Generalized Linear Model as the model which on average generated the best results.
    Keywords: quantitative investment strategies, machine learning, neural networks, regression trees, random forests, support vector machine, technical analysis, equity stock indices, developed and emerging markets, information ratio
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2021
  2. By: Xinyu Li
    Abstract: Over the past decades, breakthroughs such as Reinforcement Learning (RL) and Agent-based modeling (ABM) have made simulations of economic models feasible. Recently, there has been increasing interest in applying ABM to study the impact of residential preferences on neighborhood segregation in the Schelling Segregation Model. In this paper, RL is combined with ABM to simulate a modified Schelling Segregation model, which incorporates moving expenses as an input parameter. In particular, deep Q network (DQN) is adopted as RL agents' learning algorithm to simulate the behaviors of households and their preferences. This paper studies the impact of moving expenses on the overall segregation pattern and its role in social integration. A more comprehensive simulation of the segregation model is built for policymakers to forecast the potential consequences of their policies.
    Date: 2022–11
  3. By: Michał Lewandowski (Faculty of Economic Sciences, University of Warsaw); Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw)
    Abstract: High-quality football predictive models can be very useful and profitable. Therefore, in this research, we undertook to construct machine learning models to predict football outcomes in games from Spanish LaLiga and then we compared them with historical forecasts extracted from bookmakers, which knowledge is commonly considered to be deep and high-quality. The aim of the paper was to design models with the highest possible predictive performances, get results close to bookmakers or even building better estimators. The work included detailed feature engineering based on previous achievements of this domain and own proposals. A built and selected set of variables was used with four machine learning methods, namely Random Forest, AdaBoost, XGBoost and CatBoost. The algorithms were compared based on: Area Under the Curve (AUC) and Ranked Probability Score (RPS). RPS was used as a benchmark in the comparison of estimated probabilities from trained models and forecasts from bookmakers' odds. For a deeper understanding and explanation of the demonstrated methods, which are considered as black-box approaches, Permutation Feature Importance (PFI) was used to evaluate the impacts of individual variables. Features extracted from bookmakers odds’ occurred the most important in terms of PFI. Furthermore, XGBoost achieved the best results on the validation set (RPS equals 0.1989), which obtained similar predictive power to bookmakers' odds (their RPS between 0.1977 and 0.1984). Results of the trained estimators were promising and this article showed that competition with bookmakers is possible using demonstrated techniques.
    Keywords: predicting football outcomes, machine learning, betting, adaboost, random forest, xgboost, catboost, ranked probability score, auc, permutation feature importance
    JEL: C13 C51 C52 C53 C61 L83 Z29
    Date: 2021
  4. By: Amit Chaudhary; Daniele Pinna
    Abstract: We assess the market risk of the 0VIX lending protocol using a multi-asset agent-based model to simulate ensembles of users subject to price-driven liquidation risk. Our multi-asset methodology shows that the protocol's systemic risk is small under stress and that enough collateral is always present to underwrite active loans. Our simulations use a wide variety of historical data to model market volatility and run the agent-based simulation to show that even if all the assets like ETH, BTC and MATIC increase their hourly volatility by more than ten times, the protocol carries less than 0.1\% default risk given suggested protocol parameter values for liquidation loan-to-value ratio and liquidation incentives.
    Date: 2022–11
  5. By: Südbeck, Insa; Mindlina, Julia; Schnabel, André; Helber, Stefan
    Abstract: Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
    Keywords: Recurrent neural networks; Milkrun material supply; Stochastic flow lines; Gradient search; Simulated annealing
    JEL: C44 C45 M11
    Date: 2022–11
  6. By: Kea Baret (University of Strasbourg); Amelie Barbier-Gauchard (University of Strasbourg); Theophilos Papadimitriou (Democritus University of Thrace)
    Abstract: Since the reinforcement of the Stability and Growth Pact (1996), the European Commission closely monitors public finance in the EU members. A failure to comply with the 3% limit rule on the public deficit by a country triggers an audit. In this paper, we present a Machine Learning based forecasting model for the compliance with the 3% limit rule. To do so, we use data spanning the period from 2006 to 2018 (a turbulent period including the Global Financial Crisis and the Sovereign Debt Crisis) for the 28 EU member states. A set of eight features are identified as predictors from 138 variables through a feature selection procedure. The forecasting is performed using the Support Vector Machines (SVM). The proposed model reached 91.7% forecasting accuracy and outperformed the Logit model that was used as benchmark.
    Keywords: Fiscal Rules, Fiscal Compliance, Stability and Growth Pact, Machine learning.
    JEL: F
    Date: 2022
  7. By: Defu Cao; Yousef El-Laham; Loc Trinh; Svitlana Vyetrenko; Yan Liu
    Abstract: In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen scenarios. In this work, we utilize a multi-agent market simulator to build a synthetic LOB dataset, named DSLOB, with and without market stress scenarios, which allows for the design of controlled distributional shift benchmarking. Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods. Our results reflect the need for increased researcher efforts to develop algorithms with robustness to distributional shifts in high-frequency time series data.
    Date: 2022–11
  8. By: Thanh Trung Huynh; Minh Hieu Nguyen; Thanh Tam Nguyen; Phi Le Nguyen; Matthias Weidlich; Quoc Viet Hung Nguyen; Karl Aberer
    Abstract: Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{ 3/estimate}.
    Date: 2022–11
  9. By: Youru Li; Zhenfeng Zhu; Xiaobo Guo; Shaoshuai Li; Yuchen Yang; Yao Zhao
    Abstract: Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied in healthcare and finance. In recent years, deep learning models, especially Long Short-Term Memory neural networks (LSTMs), have led to superior performances in such sequence representation learning tasks. Despite that some attention or self-attention based models with time-aware or feature-aware enhanced strategies have achieved better performance compared with other temporal modeling methods, such improvement is limited due to a lack of guidance from global view. To address this issue, we propose a novel end-to-end Hierarchical Global View-guided (HGV) sequence representation learning framework. Specifically, the Global Graph Embedding (GGE) module is proposed to learn sequential clip-aware representations from temporal correlation graph at instance level. Furthermore, following the way of key-query attention, the harmonic $\beta$-attention ($\beta$-Attn) is also developed for making a global trade-off between time-aware decay and observation significance at channel level adaptively. Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view. Experimental results on a benchmark dataset for healthcare risk prediction, and a real-world industrial scenario for Small and Mid-size Enterprises (SMEs) credit overdue risk prediction in MYBank, Ant Group, have illustrated that the proposed model can achieve competitive prediction performance compared with other known baselines.
    Date: 2022–11
  10. By: Chinonso Nwankwo; Nneka Umeorah; Tony Ware; Weizhong Dai
    Abstract: We propose a deep learning method for solving the American options model with a free boundary feature. To extract the free boundary known as the early exercise boundary from our proposed method, we introduce the Landau transformation. For efficient implementation of our proposed method, we further construct a dual solution framework consisting of a novel auxiliary function and free boundary equations. The auxiliary function is formulated to include the feed forward deep neural network (DNN) output and further mimic the far boundary behaviour, smooth pasting condition, and remaining boundary conditions due to the second-order space derivative and first-order time derivative. Because the early exercise boundary and its derivative are not a priori known, the boundary values mimicked by the auxiliary function are in approximate form. Concurrently, we then establish equations that approximate the early exercise boundary and its derivative directly from the DNN output based on some linear relationships at the left boundary. Furthermore, the option Greeks are obtained from the derivatives of this auxiliary function. We test our implementation with several examples and compare them to the highly accurate sixth-order compact scheme with left boundary improvement. All indicators show that our proposed deep learning method presents an efficient and alternative way of pricing options with early exercise features.
    Date: 2022–11
  11. By: Roberto Roson (Department of Economics, University Of Venice CÃ Foscari; Loyola Andalusia University; GREEN Bocconi University Milan)
    Abstract: This paper presents a methodology to account, in a computable general equilibrium model, for the presence of underemployment in an economic system. The methodology is based on the estimation of a matrix, mapping different categories of workers to levels of educational attainment. A procedure is proposed, which allows recalculating the matrix after the realization of a simulation with a CGE model, when employment levels are varied. In this way, a new matrix is made consistent with the simulation results, identifying a new equilibrium in the labor market, which entails a different combination of unemployment and underemployment.
    Keywords: Underemployment, Unemployment, Labor Market, Computable General Equilibrium Models
    JEL: C68 C82 D58 E24 I20 J21 J24 J62 J82
    Date: 2022
  12. By: Surjaningsih, Ndari; Werdaningtyas, Hesti; Rahman, Faizal; Falaqh, Romadhon
    Abstract: One of the lessons learned from the global financial crisis in 2008 was raising attention to monitoring and maintaining household vulnerability, particularly household credit risk, by using the default rate as the indicator. The indicator would be worsening at the economic recession, likewise, recently happened caused by the pandemic. The default event has a complex nonlinearity relationship among the determinants. To tackle the complex relationship, this study suggests exploiting machine learning approach in modeling the probability of default, especially the individual and ensemble classifiers. Therefore, this study aims to investigate changes of the Indonesian household financial resilience before and during the pandemic, supported by the individual-level data of the Financial Information Service System. This study finds that the ensemble classifiers, notably extreme gradient boosting, have a more predominant performance than the individual classifiers. The best model, then has the feature importance analysis to identify the variable pattern in explaining the default event periodically which reveals the pattern changes before and during the pandemic. The cost of debt/repayment capability and the policy mix is significant in explaining the default event. At the same time, the project location feature weakens in discriminating the target class.
    Date: 2022–07–23
  13. By: Erhan Bayraktar; Qi Feng; Zhaoyu Zhang
    Abstract: In this work, we study the deep signature algorithms for path-dependent FBSDEs with reflections. We follow the backward scheme in [Hur\'e-Pham-Warin. Mathematics of Computation 89, no. 324 (2020)] for state-dependent FBSDEs with reflections, and combine it with the signature layer to solve American type option pricing problems while the payoff function depends on the whole paths of the underlying forward stock process. We prove the convergence analysis of our numerical algorithm and provide numerical example for Amerasian option under the Black-Scholes model.
    Date: 2022–11
  14. By: Tranos, Emmanouil; Incera, Andre Carrascal; Willis, George
    Abstract: Despite the importance of interregional trade for building effective regional economic policies, there is very little hard data to illustrate such interdependencies. We propose here a novel research framework to predict interregional trade flows by utilising freely available web data and machine learning algorithms. Specifically, we extract hyperlinks between archived websites in the UK and we aggregate these data to create an interregional network of hyperlinks between geolocated and commercial webpages over time. We also use some existing interregional trade data to train our models using random forests and then make out-of-sample predictions of interregional trade flows using a rolling-forecasting framework. Our models illustrative great predictive capability with $R^2$ greater than 0.9. We are also able to disaggregate our predictions in terms of industrial sectors, but also at a sub-regional level, for which trade data are not available. In total, our models provide a proof of concept that the digital traces left behind by physical trade can help us capture such economic activities at a more granular level and, consequently, inform regional policies.
    Date: 2022–07–06
  15. By: Xiao-Yang Liu; Ziyi Xia; Jingyang Rui; Jiechao Gao; Hongyang Yang; Ming Zhu; Christina Dan Wang; Zhaoran Wang; Jian Guo
    Abstract: Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage. In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we will provide hundreds of market environments through an automatic pipeline that collects dynamic datasets from real-world markets and processes them into gym-style market environments. Second, we reproduce popular papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, FinRL-Meta provides tens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. FinRL-Meta is available at: /FinRL-Meta
    Date: 2022–11
  16. By: Jan Hagemejer (Faculty of Economic Sciences, University of Warsaw); Maria Dunin-Wąsowicz (European Movement Forum); Jan Jakub Michałek (Faculty of Economic Sciences, University of Warsaw); Jacek Szyszka (Faculty of Economic Sciences, University of Warsaw)
    Abstract: We use a global computable general equilibrium (CGE) model to analyze several scenarios of Brexit to assess it on the EU New Member States (NMS) to complement the literature exist. Our scenarios are based on expected outcomes of the negotiations, ie. the Soft Brexit with a limited FTA and a Hard Brexit governed by WTO MFN rules. The shocks imposed on the CGE model include modifications of both tariff and non-tariff barriers. While the former is based on actual tariff data, the latter are estimated using an econometric model for both merchandise trade and services. Our results show the macroeconomic effects of Brexit are mild with a slight decline of NMS GDP of roughly 0.4 % even in the case of a Hard Brexit. However, there are some sectors that may experience somewhat significant drops in output, in particular the food sector and some other manufacturing export-oriented sectors.
    Keywords: CGE modelling, international trade, Brexit, trade policy
    JEL: F17 F10 F13
    Date: 2021
  17. By: Dylan Radovic; Lucas Kruitwagen; Christian Schroeder de Witt; Ben Caldecott; Shane Tomlinson; Mark Workman
    Abstract: The energy transition potentially poses an existential risk for major international oil companies (IOCs) if they fail to adapt to low-carbon business models. Projections of energy futures, however, are met with diverging assumptions on its scale and pace, causing disagreement among IOC decision-makers and their stakeholders over what the business model of an incumbent fossil fuel company should be. In this work, we used deep multi-agent reinforcement learning to solve an energy systems wargame wherein players simulate IOC decision-making, including hydrocarbon and low-carbon investments decisions, dividend policies, and capital structure measures, through an uncertain energy transition to explore critical and non-linear governance questions, from leveraged transitions to reserve replacements. Adversarial play facilitated by state-of-the-art algorithms revealed decision-making strategies robust to energy transition uncertainty and against multiple IOCs. In all games, robust strategies emerged in the form of low-carbon business models as a result of early transition-oriented movement. IOCs adopting such strategies outperformed business-as-usual and delayed transition strategies regardless of hydrocarbon demand projections. In addition to maximizing value, these strategies benefit greater society by contributing substantial amounts of capital necessary to accelerate the global low-carbon energy transition. Our findings point towards the need for lenders and investors to effectively mobilize transition-oriented finance and engage with IOCs to ensure responsible reallocation of capital towards low-carbon business models that would enable the emergence of fossil fuel incumbents as future low-carbon leaders.
    Date: 2022–11
  18. By: Kanazawa, Kyogo (University of Tokyo); Kawaguchi, Daiji (University of Tokyo); Shigeoka, Hitoshi (Simon Fraser University); Watanabe, Yasutora (University of Tokyo)
    Abstract: We examine the impact of Articial Intelligence (AI) on productivity in the context of taxi drivers. The AI we study assists drivers with finding customers by suggesting routes along which the demand is predicted to be high. We find that AI improves drivers' productivity by shortening the cruising time, and such gain is accrued only to low-skilled drivers, narrowing the productivity gap between high- and low-skilled drivers by 14%. The result indicates that AI's impact on human labor is more nuanced and complex than a job displacement story, which was the primary focus of existing studies.
    Keywords: artificial intelligence, skill, productivity, taxi-drivers, prediction, demand forecasting, machine learning
    JEL: J22 J24 L92 R41
    Date: 2022–10
  19. By: Zequn Jin; Lihua Lin; Zhengyu Zhang
    Abstract: This paper proposes a new class of heterogeneous causal quantities, named \textit{outcome conditioned} average structural derivatives (OASD) in a general nonseparable model. OASD is the average partial effect of a marginal change in a continuous treatment on the individuals located at different parts of the outcome distribution, irrespective of individuals' characteristics. OASD combines both features of ATE and QTE: it is interpreted as straightforwardly as ATE while at the same time more granular than ATE by breaking the entire population up according to the rank of the outcome distribution. One contribution of this paper is that we establish some close relationships between the \textit{outcome conditioned average partial effects} and a class of parameters measuring the effect of counterfactually changing the distribution of a single covariate on the unconditional outcome quantiles. By exploiting such relationship, we can obtain root-$n$ consistent estimator and calculate the semi-parametric efficiency bound for these counterfactual effect parameters. We illustrate this point by two examples: equivalence between OASD and the unconditional partial quantile effect (Firpo et al. (2009)), and equivalence between the marginal partial distribution policy effect (Rothe (2012)) and a corresponding outcome conditioned parameter. Because identification of OASD is attained under a conditional exogeneity assumption, by controlling for a rich information about covariates, a researcher may ideally use high-dimensional controls in data. We propose for OASD a novel automatic debiased machine learning estimator, and present asymptotic statistical guarantees for it. We prove our estimator is root-$n$ consistent, asymptotically normal, and semiparametrically efficient. We also prove the validity of the bootstrap procedure for uniform inference on the OASD process.
    Date: 2022–11
  20. By: Monks, Thomas; Harper, Alison; Anagnostou, Anastasia; Taylor, Simon J.E.
    Abstract: This paper provides a framework for conceptualising levels of open science and open working within computer modelling and simulation. We aim to support researchers to share their models and working so that others are free to use, reproduce, adapt and build upon, and re-share their work. We introduce a six level framework of increasing complexity: not open, open access, open artefacts, open models, open environment and open infrastructure. For each we provide practical advice on what aspects of open science researchers must consider, what options are available to them, and what challenges they will need to overcome. We illustrate our open science framework using a stylised discrete-event simulation model. All code used in this paper is available, cloud executable and reusable under an MIT license.
    Date: 2022–07–14

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