Results 11 
19 of
19
The reverse engineering of economic systems. Tools and methodology
"... First daft for further version of this work please se: www.ecople.org We will start * highlighting the methodological necessity to use reverse engineering for economic system when there is a strong relation between the whole and the part (Kaneko, 1998). In "complex systems " when there is ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
First daft for further version of this work please se: www.ecople.org We will start * highlighting the methodological necessity to use reverse engineering for economic system when there is a strong relation between the whole and the part (Kaneko, 1998). In "complex systems " when there is a complementarity between the whole and parts, we need some logic about why rules appear for a class of economic systems. This will permit a better understanding of the ways in which agents interact, especially in a dynamic socioeconomic context. A large part of studies focus on how rules give rise to dynamic behaviour, while in complex system studies, we have to study how rules form from dynamic behaviour. In searching for a universal class of behaviours for reconstructing the real economic behaviour, there are very few studies in this direction. We will use a "constructive economics " approach trying the emergence of syntactic rule from complex behaviour (Langton 1995). We will start considering what meaning Reverse Engineering have for Economics on the base of features of economic systems. This meaning impose that for the adequacy of Topdown and Bottomup a new approach Chaotic Itinerancy (Kaneko Tsuda 1994) for integrating them will be suggested. Applying RE approach to economics, we need two kinds of tools. Some aggregated time series analysis tools for which we will propose Recurrence Analysis for its ability to work with few data and to detect chaos and chaotic itinerancy (Itoh M., Kimoto M., 1997). A simulation tool that will start from this metaanalysis for obtaining the results that we considered meaningful. Of course, the here proposed RE must make use of a simulation tool that will reproduce economic time series of production, consumption, work as results, starting from the economic choices and behaviour of agents. A new simulation model based on agents is proposed. This model introduces a microfoundation macroeconomic approach in a model on Automated trading (Gulyás 2001). In the Appendix there are some information about the simulation model.
Analog computation with dynamical systems
"... A b s t r a c t Physical systems exhibit various levels of complexity: their long term dynamics may converge to fixed points or exhibit complex chaotic behavior. This paper presents a theory that enables to interpret natural processes as special purpose analog computers. Since physical systems are n ..."
Abstract
 Add to MetaCart
(Show Context)
A b s t r a c t Physical systems exhibit various levels of complexity: their long term dynamics may converge to fixed points or exhibit complex chaotic behavior. This paper presents a theory that enables to interpret natural processes as special purpose analog computers. Since physical systems are naturally described in continuous time, a definition of computational complexity for continuous time systems is required. In analogy with the classical discrete theory we develop fundamentals of computational complexity for dynamical systems, discrete or continuous in time, on the basis of an intrinsic time scale of the system. Dissipative dynamical systems are classified into the computational complexity classes Pd, CoRPd, NPd and EXP,t, corresponding to their standard counterparts, according to the complexity of their long term behavior. The complexity of chaotic attractors relative to regular ones leads to the conjecture Pa:fi NPj. Continuous time flows have been proven useful in solving various practical problems. Our theory provides the tools for an algorithmic analysis of such flows. As an example we analyze the
Agent Models of Supply Network Dynamics  Analysis, Design, and Operation
, 2001
"... : Realworld industrial supply networks are highly complex structures made up of a multitude of competing individual companies. Today's structures span the whole planet and link processes over a timeline what measures in months. In this article we focus on one of the most complex networks, t ..."
Abstract
 Add to MetaCart
: Realworld industrial supply networks are highly complex structures made up of a multitude of competing individual companies. Today's structures span the whole planet and link processes over a timeline what measures in months. In this article we focus on one of the most complex networks, the supply in the automotive industry. The observed dynamics emerge from the physical and virtual interactions of the individual components of the supply network. The complexity of the net makes an analytic description of the systemlevel behavior infeasible. Instead, we have to resort to models of the individual dynamics that are then explored in simulation experiments. In this article we compare two modeling approaches  equationbased and agentbased modeling  and we report on two research projects at ERIM's Center for Electronic Commerce that applied agentbased modeling in the analysis of simple supply structures. Simulation of system dynamics is a central element in supply network management research. Agentbased models of realworld supply chains can be build by domain experts that do not have to be versed in information technology (IT). Using these models, a quantitative evaluation of the impact of parameters and strategies in the supply network design can show the financial advantage of the introduction of supply network management. The bulk of this article reports on a simulation exercise at the DaimlerChrysler Corporation that identified a potential winwin situation for all partners along the supply chain if a new forecast policy is adopted. 2 Chapter # 1.
with asynchronous logic automata
"... Aligning the representation and reality of computation ..."
(Show Context)
Knowledge And Meaning ... Chaos And Complexity
, 1992
"... this memory is structured in particular ways that support different types of computation. The sections below on knowledge and meaning show several consequences of computational structure. In the most general setting, I use the word "complexity" to refer to the amount of information contain ..."
Abstract
 Add to MetaCart
(Show Context)
this memory is structured in particular ways that support different types of computation. The sections below on knowledge and meaning show several consequences of computational structure. In the most general setting, I use the word "complexity" to refer to the amount of information contained in observerresolvable equivalence classes.[17] This approach puts the burden directly on any complexity definition to explicitly state the representation employed by the observer. For processes with finite memory, the complexity is measured by the quantities labeled above by C . The general notion, i.e. without the finiteness restruction, has been referred to as the "statistical complexity" in order to distinguish it from the ChaitinKolmogorov complexity,[33,28] the LempelZiv complexity,[34] Rissanen's stochastic complexity, [35] and others[36,37] which are all equivalent in the limit of long data streams to the process's KolmogorovSinai entropy h ¯ i ~ X j
AgentBased Modeling vs. EquationBased Modeling: A Case Study and Users ’ Guide
"... Abstract. In many domains, agentbased system modeling competes with equationbased approaches that identify system variables and evaluate or integrate sets of equations relating these variables. The distinction has been of great interest in a project that applies agentbased modeling to industrial ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract. In many domains, agentbased system modeling competes with equationbased approaches that identify system variables and evaluate or integrate sets of equations relating these variables. The distinction has been of great interest in a project that applies agentbased modeling to industrial supply networks, since virtually all computerbased modeling of such networks up to this point has used system dynamics, an approach based on ordinary differential equations (ODE’s). This paper summarizes the domain of supply networks and illustrates how they can be modeled both with agents and with equations. It summarizes the similarities and differences of these two classes of models, and develops criteria for selecting one or the other approach. 1.
Cellular Computing and Least Squares for partial differential problems parallel solving
, 2009
"... This paper shows how partial differential problems can be solved thanks to cellular computing and an adaptation of the Least Squares Finite Elements Method. As cellular computing can be implemented on distributed parallel architectures, this method allows the distribution of a resource demanding dif ..."
Abstract
 Add to MetaCart
(Show Context)
This paper shows how partial differential problems can be solved thanks to cellular computing and an adaptation of the Least Squares Finite Elements Method. As cellular computing can be implemented on distributed parallel architectures, this method allows the distribution of a resource demanding differential problem over a computer network.
Cellular Neural Networks and Least Squares for partial differential problems parallel solving
, 2009
"... This paper shows how Cellular Neural Networks (CNN) can be harnessed into solving partial differential problems through an adaptation of the Least Squares Finite Elements Method. As CNNs can be implemented on distributed parallel architectures, this method allows the distribution of a resource deman ..."
Abstract
 Add to MetaCart
This paper shows how Cellular Neural Networks (CNN) can be harnessed into solving partial differential problems through an adaptation of the Least Squares Finite Elements Method. As CNNs can be implemented on distributed parallel architectures, this method allows the distribution of a resource demanding differential problem over a computer network.
On the Applications of Cellular Automata and Artificial Life
"... Abstract — Cellular automata are dynamical systems which emulate natural evolution. Cellular automata is a part of Artificial Life. The paper explains the basics of Artificial Life and Cellular Automata. It also examines the basic building block of such systems that is Langton’s Loops. The paper dis ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract — Cellular automata are dynamical systems which emulate natural evolution. Cellular automata is a part of Artificial Life. The paper explains the basics of Artificial Life and Cellular Automata. It also examines the basic building block of such systems that is Langton’s Loops. The paper discusses various applications of Artificial Life and Cellular Automata and also intends to present a brief review of the work that has been done so far and the gaps there in. The last section of the paper discusses the applicability of Artificial Life in other spheres.