Empirical Validation of Agent Based Models: A Critical Survey

This paper addresses the problem of finding the appropriate method for conducting empirical validation in agent-based (AB) models, which is often regarded as the Achilles’ heel of the AB approach to economic modelling. The paper has two objectives. First, to identify key issues facing AB economists engaged in empirical validation. Second, to critically appraise the extent to which alternative approaches deal with these issues. We identify a first set of issues that are common to both AB and neoclassical modellers and a second set of issues which are specific to AB modellers. This second set of issues is captured in a novel taxonomy, which takes into consideration the nature of the object under study, the goal of the analysis, the nature of the modelling assumptions, and the methodology of the analysis. Having identified the nature and causes of heterogeneity in empirical validation, we examine three important approaches to validation that have been developed in AB economics: indirect calibration, the Werker-Brenner approach, and the history-friendly approach. We also discuss a set of open questions within empirical validation. These include the trade-off between empirical support and tractability of findings, the issue of over-parameterisation, unconditional objects, counterfactuals, and the non-neutrality of data.Empirical validation, agent-based models, calibration, history-friendly modelling

Kompetensi pembimbing syarikat bertauliah Sistem Latihan Dual Nasional (SLDN)

Sistem Latihan Dual Nasional (SLDN) merupakan satu sistem latihan dan usahasama antara sektor awam dan sektor swasta dilaksanakan untuk melahirkan tenaga mahir k-worker selari dengan keperluan industri masa kini untuk membangunkan ekonomi negara. Pihak kerajaan dan syarikat swasta menaja pekerja pilihan mereka sebagai pelatih dalam sistem latihan ini bagi mempertingkatkan kebolehan pekerja mereka. Selain itu, pelatih juga terdiri daripada pelajar yang tidak dapat melanjutkan pelajaran ke mana-mana institusi pengajian tinggi awam mahupun swasta. Sistem ini menjalankan pendekatan day release iaitu pelatih menjalani latihan selama empat hari di industri dan satu hari di institusi latihan atau block release iaitu pelatih menjalani latihan kemahiran di industri empat bulan dan satu bulan di institusi latihan mengikut kesesuaian industri tersebut. Kajian berbentuk deskriptif dijalankan untuk melihat melihat tahap kompetensi pembimbing SLDN. Selain itu juga, kajian ini dijalankan bagi melihat perbezaan terhadap tahap pengetahuan, kemahiran dan sikap pembimbing SLDN berdasarkan jantina. Kajian ini juga dibuat bagi menentukan hubungan kompetensi pembimbing berdasarkan pengalaman bekerja. Penyelidikan tinjauan deskriptif ini menggunakan borang soal selidik sebagai instrumen kajian berskala Likert. Seramai 84 orang responden yang terdiri daripada pembimbing syarikat bertauliah SLDN terlibat di dalam kajian ini. Data dianalisis menggunakan SPSS versi 16.0. Hasil analisis mendapati pembimbing mempunyai pengetahuan yang tinggi di samping kemahiran dan sikap. Keputusan inferensi pula menunjukkan tidak terdapat perbezaan antara tahap pengetahuan, kemahiran dan sikap pembimbing berdasarkan jantina manakala analisis korelasi Pearson menunjukkan tidak terdapat hubungan antara kompetensi pembimbing berdasarkan pengalaman bekerja

Kompetensi pembimbing syarikat bertauliah Sistem Latihan Dual Nasional (SLDN)

Sistem Latihan Dual Nasional (SLDN) merupakan satu sistem latihan dan usahasama antara sektor awam dan sektor swasta dilaksanakan untuk melahirkan tenaga mahir k-worker selari dengan keperluan industri masa kini untuk membangunkan ekonomi negara. Pihak kerajaan dan syarikat swasta menaja pekerja pilihan mereka sebagai pelatih dalam sistem latihan ini bagi mempertingkatkan kebolehan pekerja mereka. Selain itu, pelatih juga terdiri daripada pelajar yang tidak dapat melanjutkan pelajaran ke mana-mana institusi pengajian tinggi awam mahupun swasta. Sistem ini menjalankan pendekatan day release iaitu pelatih menjalani latihan selama empat hari di industri dan satu hari di institusi latihan atau block release iaitu pelatih menjalani latihan kemahiran di industri empat bulan dan satu bulan di institusi latihan mengikut kesesuaian industri tersebut. Kajian berbentuk deskriptif dijalankan untuk melihat melihat tahap kompetensi pembimbing SLDN. Selain itu juga, kajian ini dijalankan bagi melihat perbezaan terhadap tahap pengetahuan, kemahiran dan sikap pembimbing SLDN berdasarkan jantina. Kajian ini juga dibuat bagi menentukan hubungan kompetensi pembimbing berdasarkan pengalaman bekerja. Penyelidikan tinjauan deskriptif ini menggunakan borang soal selidik sebagai instrumen kajian berskala Likert. Seramai 84 orang responden yang terdiri daripada pembimbing syarikat bertauliah SLDN terlibat di dalam kajian ini. Data dianalisis menggunakan SPSS versi 16.0. Hasil analisis mendapati pembimbing mempunyai pengetahuan yang tinggi di samping kemahiran dan sikap. Keputusan inferensi pula menunjukkan tidak terdapat perbezaan antara tahap pengetahuan, kemahiran dan sikap pembimbing berdasarkan jantina manakala analisis korelasi Pearson menunjukkan tidak terdapat hubungan antara kompetensi pembimbing berdasarkan pengalaman bekerja

Simulating the Social Processes of Science

Science is the result of a substantially social process. That is, science relies on many inter-personal processes, including: selection and communication of research findings, discussion of method, checking and judgement of others' research, development of norms of scientific behaviour, organisation of the application of specialist skills/tools, and the organisation of each field (e.g. allocation of funding). An isolated individual, however clever and well resourced, would not produce science as we know it today. Furthermore, science is full of the social phenomena that are observed elsewhere: fashions, concern with status and reputation, group-identification, collective judgements, social norms, competitive and defensive actions, to name a few. Science is centrally important to most societies in the world, not only in technical, military and economic ways, but also in the cultural impacts it has, providing ways of thinking about ourselves, our society and our environment. If we believe the following: simulation is a useful tool for understanding social phenomena, science is substantially a social phenomenon, and it is important to understand how science operates, then it follows that we should be attempting to build simulation models of the social aspects of science. This Special Section of <i>JASSS</i> presents a collection of position papers by philosophers, sociologists and others describing the features and issues the authors would like to see in social simulations of the many processes and aspects that we lump together as "science". It is intended that this collection will inform and motivate substantial simulation work as described in the last section of this introduction.Simulation, Science, Science and Technology Studies, Philosophy, Sociology, Social Processes

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

Does Empirical Embeddedness Matter? Methodological Issues on Agent-Based Models for Analytical Social Science

The paper deals with the use of empirical data in social science agent-based models. Agent-based models are too often viewed just as highly abstract thought experiments conducted in artificial worlds, in which the purpose is to generate and not to test theoretical hypotheses in an empirical way. On the contrary, they should be viewed as models that need to be embedded into empirical data both to allow the calibration and the validation of their findings. As a consequence, the search for strategies to find and extract data from reality, and integrate agent-based models with other traditional empirical social science methods, such as qualitative, quantitative, experimental and participatory methods, becomes a fundamental step of the modelling process. The paper argues that the characteristics of the empirical target matter. According to characteristics of the target, ABMs can be differentiated into case-based models, typifications and theoretical abstractions. These differences pose different challenges for empirical data gathering, and imply the use of different validation strategies.Agent-Based Models, Empirical Calibration and Validation, Taxanomy of Models

Saturated flow boiling in small- to micro- diameter metallic tubes: Experimental results and modeling

Some results of a long-term study of flow boiling patterns, heat transfer rates and pressure drop of R134a at pressures of 6-14 bar in five vertical stainless steel tubes of internal diameter 4.26, 2.88, 2.01, 1.1 and 0.52 mm are presented in this paper. The flow regimes in the 4.26 mm to 1.1 mm tubes were identified as dispersed bubble, bubbly, slug, churn, annular and mist flows. As the diameter was reduced, progressively slimmer vapour slugs, a thinner liquid film around the vapour slug and a less chaotic vapour-liquid interface in churn flow were observed. Confined flow appeared first in the 2.01 mm tube. Dispersed bubble flow was not observed in the smallest tube (0.52 mm) for the range studied in runs in which wavy film flow occurred. The heat transfer coefficients in tubes ranging from 4.26 mm down to 1.1 mm increased with heat flux and system pressure, but did not change with vapour quality for low quality values. At higher quality, the heat transfer coefficients decreased with quality, indicating local dryout. The heat transfer characteristics of the 0.52 mm tube were different from those in the larger tubes. The data fell into two groups that exhibited different influences of heat flux below and above a heat flux threshold. The pressure drop and heat transfer results were compared with existing correlations but with some limited success. Recent progress on mechanistic models for heat transfer along with comparisons and recommendations are included in the paper

Recent Progress Towards Predicting Aircraft Ground Handling Performance

The significant progress which has been achieved in development of aircraft ground handling simulation capability is reviewed and additional improvements in software modeling identified. The problem associated with providing necessary simulator input data for adequate modeling of aircraft tire/runway friction behavior is discussed and efforts to improve this complex model, and hence simulator fidelity, are described. Aircraft braking performance data obtained on several wet runway surfaces is compared to ground vehicle friction measurements and, by use of empirically derived methods, good agreement between actual and estimated aircraft braking friction from ground vehilce data is shown. The performance of a relatively new friction measuring device, the friction tester, showed great promise in providing data applicable to aircraft friction performance. Additional research efforts to improve methods of predicting tire friction performance are discussed including use of an instrumented tire test vehicle to expand the tire friction data bank and a study of surface texture measurement techniques