Nonlinearity in high-order models of social systems

Historically limitations of mathematical analysis forced exclusion of most nonlinearities from models of social systems. Computer simulation removed the pressure to focus on linear representations, but even so, data analysis and model validation methods have perpetuated a bias toward linearity in models. However, much of real-life behavior arises from nonlinearities. If models are to be good representations of social systems, there must be unrestricted willingness to incorporate nonlinearity. Otherwise, we exclude access to much of the available information about the structure and policies that cause observed behavior. A large fraction of our knowledge about social systems lies in what must necessarily happen as extreme conditions are gradually approached. The majority of functional relationships are nonlinear, as when two variables are multiplied, for example, sales rate times price to generate a payment stream. Taking advantage of knowledge about real-life nonlinearities and their crucial contribution to behavior, leads to models that endogeneously generate the principal modes of behavior that are observed in actual systems.     

Fractal regularities of sub-harmonic motions perspective for pulse dynamics monitoring

The pulse energy conversion systems demonstrate a complex dynamics at parameter variations in a wide range. The problem of one-to-one decision-making about system dynamics using a priori information in the form of the parameter diagram is considered. The step-by-step approach to the problem solution based on fractal regularities of the dynamics is proposed. The first stage is the type of motion identification; the second one is the parameter vector identification within this motion type. The parallel and sequential algorithm schemes for this approach realization are presented. The problem of dynamics evolution forecasting is suggested to consider as a pointer to future research.     

Behavior prediction of washing system in a paper industry using GA and fuzzy lambda–tau technique

Availability analysis has been an important issue in the design field of any Industrial system as the system structure has become more complicated. Also, the system availability is affected by many factors such as design, manufacturing, installation, etc., and so it may be extremely difficult to model, analyze and predict the failure behavior of the system. The purpose of this paper is to develop a new approach for computing various performance measures, namely reliability, availability, MTBF (mean time between failures), ENOF (expected number of failures), failure rate and repair time, for any industrial system. In the proposed approach, the failure rates and repair times of all constituent components are obtained using genetic algorithms and then various performance measures are computed using fuzzy lambda–tau methodology. Washing system, the major part of paper industry is the subject of study. The interactions among the working components are modeled using Petri nets. Failure and repair rates are represented using triangular fuzzy numbers as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into system model. Based on calculated reliability parameters, a structured framework has been developed that may help the maintenance engineers to analyze and predict the system behavior.     

A multicriteria approach to sustainable energy supply for the rural poor

Despite significant progress in energy technology, about two billion people worldwide, particularly the poor in rural areas of developing countries, have no access to electricity. Decision-making concerning the most appropriate energy technology for supplying these areas has been difficult; existing energy decision-support tools have been useful but are mostly incomplete. Trade-offs, as well as impacts that can be positive or negative, may emerge as a result of implementing modern forms of energy. These can affect both community’s livelihoods as well as the confidence of decision-makers in relation to alternative technologies. The paper discusses a newly designed multicriteria approach and its novel robustness analysis for selecting energy generation systems for the improvement of livelihoods in rural areas. The proposed methodology builds upon a sustainable rural livelihoods framework to address multiple interactions and calculate trade-offs aimed at boosting decision-makers’ confidence in the selected technologies. The methodology is tested via a case study in Colombia.     

A Learning Progression for Elementary Students’ Functional Thinking

In this article we advance characterizations of and supports for elementary students’ progress in generalizing and representing functional relationships as part of a comprehensive approach to early algebra. Our learning progressions approach to early algebra research involves the coordination of a curricular framework and progression, an instructional sequence, written assessments, and levels of sophistication describing students’ algebraic thinking. After detailing this approach, we focus on what we have learned about the development of students’ abilities to generalize and represent functional relationships in a grades 3–5 early algebra intervention by sharing the levels of responses we observed in students’ written work over time. We found that the sophistication of students’ responses increased over the course of the intervention from recursive patterning to correspondence and in some cases covariation relationships between variables. Students’ responses at times differed by the particular tasks that were posed. We discuss implications for research and practice.     

The Waveform Relaxation method for systems of differential/algebraic equations

This paper reports efforts towards establishing a parallel numerical algorithm known as Waveform Relaxation (WR) for simulating large systems of differential/algebraic equations. The WR algorithm was established as a relaxation based iterative method for the numerical integration of systems of ODEs over a finite time interval. In the WR approach, the system is broken into subsystems which are solved independently, with each subsystem using the previous iterate waveform as “guesses” about the behavior of the state variables in other subsystems. Waveforms are then exchanged between subsystems, and the subsystems are then resolved repeatedly with this improved information about the other subsystems until convergence is achieved.

In this paper, a WR algorithm is introduced for the simulation of generalized high-index DAE systems. As with ODEs, DAE systems often exhibit a multirate behavior in which the states vary as differing speeds. This can be exploited by partitioning the system into subsystems as in the WR for ODEs. One additional benefit of partitioning the DAE system into subsystems is that some of the resulting subsystems may be of lower index and, therefore, do not suffer from the numerical complications that high-index systems do. These lower index subsystems may therefore be solved by less specialized simulations. This increases the efficiency of the simulation since only a portion of the problem must be solved with specially tailored code. In addition, this paper established solvability requirements and convergence theorems for varying index DAE systems for WR simulation.     

Stabilization of unknown nonlinear systems using a cognition-based framework

This paper considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems. In order to fulfill the task of stabilization, neither the information about the systems dynamical structure nor the knowledge about system physical behaviors, but the system states, which are assumed as measurable, are required. The structure of the proposed controller consists of three parts. The first part is based on a recurrent neural network (RNN) to be used for local identification of the unknown nonlinear system in real time. The network can be utilized as system characteristics, which is further used to design the controller within the third part. In the second part, the set of the given input values leading to stable behavior of the closed-loop system will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, a suitable control input value is chosen accordingly to a time-relevant criteria from the set of input values generated in the second part of the controller. These three parts and their internal connections are arranged within a so-called cognition framework. The proposed cognitive controller is able to gain useful knowledge (with local validity) and define autonomously a suitable control input with respect to the requirements of the time-relevant criteria. Numerical examples are shown to demonstrate the successful application and performance of the method. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A simulation optimization method that considers uncertainty and multiple performance measures

Simulation optimization provides a structured approach to system design and configuration when analytical expressions for input/output relationships are unavailable. This research focuses on the development of a new simulation optimization technique applicable to systems having multiple performance measures. The aim of this research is to incorporate a simulation end user’s preference towards risk and uncertainty into the search process for the best decision alternative. Automation of the optimization procedure is a necessity. Therefore, this paper proposes a simulation optimization method that involves a preference model, specifically adapted for decision making with simulation models.     

Analytical failure-detection using adpative,secondary observers

The problem of designing analytical failure-detection systems, using adaptive observers, is addressed in this paper. Failure-detection systems can be applied to linear multi-input, multi-output systems and are related to the examination of then-dimensional observer error vector which carries the necessary information on possible failures. This approach leads toward the design of highly sensitive failure detection systems, obtaining a unique fingerprint for every possible failure (abrupt or soft). In order to keep the observer's false-alarm rate under a certain specified value, it is necessary to have an acceptable matching between the observer model and the system parameters. It is shown here that properly designed adaptive observers are able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability. Conditions for convergence for the adaptive observer algorithm are obtained. Good tracking performance with small observer output errors, coupled with accurate and fast parameter identification in both deterministic and stochastic cases, is obtained.Dedicated to G. LeitmannThis research was supported by a National Research Council Associateship at NASA Ames Research Center. The author is indebted to both NRC and NASA Ames Research Center.     

RECURSIVE SYSTEM IDENTIFICATION

Most of existing methods in system identification with possible exception of those for linear systems are off-line in nature, and hence are nonrecursive. This paper demonstrates the recent progress in recursive system identification. The recursive identification algorithms are presented not only for linear systems （multivariate ARMAX systems） but also for nonlinear systems such as the Hammerstein and Wiener systems, and the nonlinear ARX systems. The estimates generated by the algorithms are online updated and converge a.s. to the true values as time tends to infinity.     

Modeling framework for membrane computing in biological systems: Evaluation with a case study

Membrane computing can represent the structures and behaviors of biological systems while considering their characteristics. This paper proposes a modeling framework for membrane computing in biological systems to provide guidelines when using and experimenting with membrane computing. The framework processes include the biological requirements and property specifications, membrane computing model, membrane computing simulation strategy, and model checking approach. A biological system that comprised the ligand–receptor networks of TGF-β protein was used as a case study to evaluate the framework. The evaluation of the framework demonstrated that membrane computing performed better than conventional ordinary differential equations when capturing the structure and behavior of biological systems.     

Inexact de Novo programming for water resources systems planning

This study presents an interval de Novo programming (IDNP) approach for the design of optimal water-resources-management systems under uncertainty. The model is derived by incorporating the existing interval programming and de Novo programming, allowing uncertainties represented as intervals within the optimization framework. The developed IDNP approach has the advantages in constructing optimal system design via an ideal system by introducing the flexibility toward the available resources in the system constraints. A simple numerical example is introduced to illustrate the IDNP approach. The IDNP is then applied to design an inexact optimal system with budget limit instead of finding the optimum in a given system with fixed resources in a water resources planning case. The results demonstrate that the developed method efficiently produces stable solutions under different objectives. Optimal supplies of good-quality water are obtained in considering different revenue targets of municipal–industrial–agricultural competition under a given budget.     

Mixing methodologies for information systems development and strategy: A higher education case study

Although informed approaches from an interpretivist perspective have been widely promoted over the last two decades information systems development is largely informed by a functionalist perspective, into which human-centred or interpretivist issues are incorporated as they are seen to be required. By treating such developments as critically informed social systems, this paper offers an alternative viewpoint. Firstly, a theoretical justification for such an approach is offered, drawing on developments in Critical Management Science, themselves based on a strand of Critical Social Theory. Secondly, an action research-based intervention is detailed, demonstrating the practical applicability of such an approach, an applicability that has hitherto been questioned. The outcome is a synthesised approach to operational and strategic information systems development, within a critical framework, which, it is argued, offers a greater potential for success than the currently promoted approaches.     

Coordination mechanisms of supply chain systems

Supply chain management (SCM) has become an important management paradigm. As supply chain members are often separate and independent economic entities, a key issue in SCM is to develop mechanisms that can align their objectives and coordinate their activities so as to optimize system performance. In this paper, we provide a review of coordination mechanisms of supply chain systems in a framework that is based on supply chain decision structure and nature of demand. This framework highlights the behavioral aspects and information need in the coordination of a supply chain. The identification of these issues points out several directions of future research in this area.     

A problem-structuring method for complex societal decisions: Its philosophical and psychological dimensions

A novel approach to problem-structuring for decisions relating to the sustainable development of large-scale infrastructure is presented. The philosophical (a critical realist ontology and epistemology), psychological (implications of behavioural and cognitive psychology), and systems dimensions (dynamics and emergent properties) of the problem are discussed, and form the basis of the approach. The problem is structured as a system of “trilemmas” (a means of representing and criticising sets of three competing forces), representing different aspects of the problem and considered in the context of a number of problem dimensions. A case study relating to the development of a sustainable water system for a major Australian metropolis is developed to test the value of the approach.     

Identification of the state of a moving object by observation of geophysical fields

Problems of identification of the state of a moving object by observation of the geophysical fields are considered. To correct the errors accumulated in the inertial navigation system of the object, the extremal comparison of the information about the outer geophysical fields with the a priori information about them stored in the onboard computer is used (the correlation-extremal approach). The general principles for investigation of the systems for navigation of moving objects by the outer informational fields are described. A model of navigation by the geophysical field and a method for a priori estimation of the local informativeness of the field are suggested.     

Recursive estimation: A unified approach to the identification estimation,and forecasting of hydrological systems

Mathematical models of hydrological and water-resource systems have been formulated in many different ways and with various levels of complexity. There are advantages to be gained, therefore, by trying to unify some of the more common models within a statistical framework which will allow for more objective methods of model calibration. In this paper, we consider the general class of linear, dynamic models, as applied to the characterisation of flow and dispersion behavior in rivers, and show how these can be unified within the context of recursive time-series analysis and estimation. This allows not only for more objective, data-based approaches to stochastic model structure identification, but also for improved statistical estimation and the development of both constant parameter and self-adaptive, Kalman-filter-based forecasting procedures. The unified approach presented in the paper is being applied successfully in other environmental areas, such as soil science, climatic data analysis, meterological forecasting, and plant physiology.     

Behavioral modeling in weight loss interventions

Designing systems with human agents is difficult because it often requires models that characterize agents’ responses to changes in the system’s states and inputs. An example of this scenario occurs when designing treatments for obesity. While weight loss interventions through increasing physical activity and modifying diet have found success in reducing individuals’ weight, such programs are difficult to maintain over long periods of time due to lack of patient adherence. A promising approach to increase adherence is through the personalization of treatments to each patient. In this paper, we make a contribution toward treatment personalization by developing a framework for predictive modeling using utility functions that depend upon both time-varying system states and motivational states evolving according to some modeled process corresponding to qualitative social science models of behavior change. Computing the predictive model requires solving a bilevel program, which we reformulate as a mixed-integer linear program (MILP). This reformulation provides the first (to our knowledge) formulation for Bayesian inference that uses empirical histograms as prior distributions. We study the predictive ability of our framework using a data set from a weight loss intervention, and our predictive model is validated by comparison to standard machine learning approaches. We conclude by describing how our predictive model could be used for optimization, unlike standard machine learning approaches that cannot.     

Developing Competitive Bids: A Framework For Information Processing

This paper considers information requirements for the development of a bid price in a generalized competitive-bidding situation. A framework for the bid-preparation process is described. The framework outlines the steps required to combine various types of information to produce a bid price. Particular emphasis is placed on information about competitors, criteria to be used by a contract buyer, and the buyer's method of evaluating bids. The framework incorporates the ‘maximization of expected profit’ approach much discussed in the literature, but the mathematical details of this calculation are not considered. Discussion focusses on obtaining and making use of relevant information, and on desirable modifications to expected-profit calculations.     

Operational research and sustainable development: Tackling the social dimension

Sustainable development is increasingly being seen as a major challenge in global terms. Operational Research (OR) has yet to be fully utilised in this area. To date, where it has been mostly used, it tends to deal with the relationships between environmental management and product supply chain and rarely focuses on the social dimension. This article seeks to discuss the potential of OR in the wider arena of sustainable development. It first explores the issue of widening OR responses to an inter-generational ethic. A case study is then described using a more holistic approach to explore sustainable development in the context of a city. The paper finishes with a discussion of the implications of this approach in relation to sustainable development more generally.