Mathematical Models in Philosophy of Science

Domotor, Zoltan

doi:10.1016/b978-0-08-097086-8.43057-6

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…The objective of the model validation process is to determine the extent of accuracy that a model or theory under study represents in relation to its target system (Domotor, 2015). In this context, the study shall try to present the empirical models which the reviewed articles have used to examine the proposed theories and relationships.…”

Section: Theoretical and Empirical Evidence In The Reviewed Articlesmentioning

confidence: 99%

Macroeconomic State Variables and Stock Market Performance: A Systematic Review and Future Research Agenda

Lone

Darzi

Bhat

2021

FIIB Business Review

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The current study presents a systematic review of macroeconomic risk factors and stock market performance under the broader theme of multi-factor asset pricing or the Arbitrage Pricing Theory (APT). Based on the review of relevant literature, the study summarizes key findings of different research works and identifies significant research gaps that need to be addressed by future research. It assesses the state of published work in the domain of asset pricing by systematically analyzing 116 research articles. The research works whose focal point has been asset pricing have been retrieved from leading databases and journals related to financial economics for the period 1976–2019. The study provides a comprehensive look at the conceptual, theoretical, and methodological (or empirical) developments and patterns prevalent at the aggregate level. The implications of the study hold key importance for academia or future researchers, policy-makers and most importantly the investors for guiding their investment decisions efficiently.

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Section: Theoretical and Empirical Evidence In The Reviewed Articlesmentioning

confidence: 99%

Macroeconomic State Variables and Stock Market Performance: A Systematic Review and Future Research Agenda

Lone

Darzi

Bhat

2021

FIIB Business Review

View full text Add to dashboard Cite

show abstract

“…Once all associations are verified, testing of the model is performed for accuracy by matching the model's behaviors with the observations in which the model is built from. This step is mandatory as it determines the degree in which the model is an accurate representation of the system under study [3,20]. It is common in mathematical models that stochastic variations arise from imprecisions in floating-point calculations, which are commonly known as rounding-off errors.…”

Section: Am Inclined Towards Frigg and Reissmentioning

confidence: 99%

“…This step is mandatory as it determines the degree in which the model is an accurate representation of the system under study [3,20]. It is common in mathematical models that stochastic variations arise from imprecisions in floating-point calculations, which are commonly known as rounding-off errors.…”

Section: /7mentioning

confidence: 99%

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Of (Biological) Models and Simulations

Ling¹

2016

MOJPB

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All models are wrong, but some are useful. Box and Draper [1].All models are right… Most are useless. Tarpey [2]The role of models has been recognized as an important aspect to the scientific method [3] for more than 70 years [4]. In recent years, there are increasing interests in metabolic modeling in biology [5,6]. The process of building a model is known as modeling. Generally speaking, models can be classified as either material models or formal models. Rosenblueth & Wiener [7] consider material models to be a physical representation of the object under study whereas formal models are symbolic or logical representations of the object under study. For example, a scale down model of an airplane for testing its aerodynamic properties in a wind tunnel is a material model. The main purpose of a scaleddown material model is the ease of study, which had been clearly demonstrated by a study on fireplace more than 200 years ago [8].A formal model, on the other hand, is an abstract but unambiguous description of a physical object or phenomenon, usually in logical or mathematical constructs. Commonly used mathematical constructs for building formal models are rate equations, such as differential equations [9], or state transitions, such as petri nets [10]. Other modeling formalisms had been reviewed by Machado [11]. Besides differentiating models by their underlying formalism, models can also be classified by their informational complexity. At the lowest level, the presence or absence of associations between 2 entities (such as cells or molecules) can be represented as a Boolean term. This results in a binary state petri net or Boolean network, which can be used to study cycles and reachability. For example, a metabolic pathway or gene regulation network is essentially a Boolean network [12]. At the next level, the stiochiometries between each state can be added [13], resulting in a steady state model, which can be simulated and used to study the state or concentrations of various molecules at homeostasis using methods such as flux balance analysis [14][15][16]. At the highest level, the transitions between each state can be represented using a set of differential equations and this result in a kinetic model [17]. The main advantage of kinetic models over steady state model is the ability to simulate each state (or molecules in the context of metabolic modeling) across time, in addition to the ability to model homeostasis [12]. This will allow for identification of bottlenecks in the metabolism [18].With advancements in computing power, formal models, being constructed in mathematical or logical format, render it possible for computation. Such computation or execution of formal models is commonly known as simulation. Hence, by definition, one can "build a model", then "run or execute a model" or "run or execute a simulation" but in no means, one can "build a simulation". The ability to render execution of a model; thus, an executable or simulatable model; defines the fundamental difference between a material model an...

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Philosophy of Probability

Wenmackers

SpringerReference

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Mathematical Models in Philosophy of Science

Cited by 3 publications

References 9 publications

Macroeconomic State Variables and Stock Market Performance: A Systematic Review and Future Research Agenda

Macroeconomic State Variables and Stock Market Performance: A Systematic Review and Future Research Agenda

Of (Biological) Models and Simulations

Philosophy of Probability

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