Formal specification terminology for demographic agent-based models of fixed-step single-clocked simulations

This document presents adequate formal terminology for the mathematical specification of a subset of Agent Based Models (ABMs) in the field of Demography. The simulation of the targeted ABMs follows a fixed-step single-clocked pattern. The proposed terminology further improves the model understanding and can act as a stand-alone methodology for the specification and optionally the documentation of a significant set of (demographic) ABMs. Nevertheless, it is imaginable the this terminology probably with further extensions can be merged with the largely-informal widely-used model documentation and communication O.D.D. protocol [Grimm and et al., 2020, Amouroux et al., 2010] to reduce many sources of ambiguity, hindering model replications by other modelers. A published demographic model documentation, largely simplified version of the Lone Parent Model [Gostoli and Silverman, 2020] is separately published in [Elsheikh, 2023b] as illustration for the formal terminology. The model was implemented in the Julia language [Elsheikh, 2023a] based on the Agents.jl julia package [Datseris et al., 2022].Comment: arXiv admin note: substantial text overlap with arXiv:2307.1654

Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences

The execution and runtime performance of model-based analysis tools for realistic large-scale ABMs (Agent-Based Models) can be excessively long. This due to the computational demand exponentially proportional to the model size (e.g. Population size) and the number of model parameters. Even the runtime of a single simulation of a realistic ABM may demand huge computational resources when attempting to employ realistic population size. The main aim of this ad-hoc brief report is to highlight some of surrogate models that were adequate and computationally less demanding for nonlinear dynamical models in various modeling application areas.To the author knowledge, these methods have been not, at least extensively, employed for ABMs within the field of (SHCS) Social Health Computational Sciences, yet. Thus, they might be, but not necessarily, useful in progressing state of the art for establishing surrogate models for ABMs in the field of SHCS.Comment: 4 page

The structural index of sensitivity equation systems

This work presents a new methodology for computing parameter sensitivities of differential algebraic system of equations with higher differential index. This methodology is particularly adequate for performing sensitivity analysis of object-oriented models described by modern universal modelling languages. By employing the same concepts and tools adopted by these languages for structural analysis of systems of equations, it is shown that the computational graphs of a differential algebraic system of equations and its corresponding sensitivity equation are structurally isomorphic. As a consequence, the structural index of both systems of equations are proven to be equal. Based on this result, an efficient strategy for index reduction of sensitivity equations is designed

Modelica based computational tools for sensitivity analysis via automatic differentiation

This work is mainly concerned with sensitivity analysis of DAE-based models described by the modern object-oriented modeling language Modelica. In this context, an automatic differentiation tool named as ADModelica is presented. It fully employs Modelica-based compiler techniques forming a new automatic differentiation approach for non-causal equation-based languages. Already existing open-source compiler tools are utilized for reducing implementation efforts. A generated output model efficiently represents a sensitivity equation system by which parameter sensitivities can be simulated using any existing Modelica simulation environment. The resulting tool has been successfully applied on high-level Modelica models in the field of Systems Biology. In benchmark examples, the performance of the generated models are better than applying common finite difference methods in terms of accuracy and runtime performance. Moreover, the representation of these models permits the exploitation of structural characteristics of sensitivity equation systems for significantly improved runtime performance on supercomputer clusters.Using ADModelica, several sensitivity analysis application studies of computationally, algorithmically and technically challenging nature have been performed towards the realization of stable efficient parameter estimation process of large and badly-scaled dynamical models. These studies cover among others: • The examination of several global multistart optimization methods w.r.t. results quality and implementation efforts, in particular the design of new derivative-based hybrid heuristic strategies• The determination of confidence regions of model parameters via identifiability analysis techniques based on linearized statistics and Monte Carlo bootstrap methods.Within this work furtherModelica-based both domain-dependent and domain-independent computational tools have been implemented such as:• A compact Modelica library for simplified kinetics for modeling complex reaction systems through which model families can be easily specified• A tool for visualizing scaled parameter sensitivities within a supervised master thesis • A Modelica-based editor for modeling biochemical reaction networks within a collaborative work with collegesFinally, this thesis also covers theoretical studies concerning the differential and the structural index of a DAE system and the corresponding sensitivity equation system with an interesting mathematically proven conclusion about their relationship