The Role of Models in Determining Policy for Transition to a more Resilient Technological Society

Modern technological societies are confronted by a vast array of problems. They are interlocked, one with another, forming a vast web. The solution to anyone problem will not necessarily ease the functioning of the whole -- indeed, it can often make things worse. This is true because the modern technological world is incredibly complex, interconnected, and interdependent

Comments on the Budworm for Forest Ecology Model

The simulation model described by STANDER is being studied to see of it can be restated as a linear programming model

Bibliography of Soviet and Western European Publications on Large-Scale Linear Programming

This bibliography originates from the Workshop on Large-Scale Linear Programming held at IIASA over the period June 2-6 1980. The Proceedings of this Workshop (edited by G.B. Dantzig, M.A.H. Dempster and M. Kallio, IIASA CP-81-S1) contains a bibliography covering North American and Western European publications. This bibliography is a supplement which covers Eastern European work on large-scale linear programming. As some important work may still be missing, further contributions (with English translations of titles published in other languages) are most welcome. Such contributions should be sent to H. Gasking at IIASA, where a computerized bibliography is maintained. Revised versions will be issued from time to time

A Case Study of Forest Ecosystem Pest Management

The boreal forests of North America have, for centuries, experienced periodic outbreaks of a defoliating insect called the Spruce Budworm. In anyone outbreak cycle a major proportion of the mature softwood forest in effected areas can die, with major consequences to the economy and employment of regions like New Brunswick, which are highly dependent on the forest industry. An extensive insecticide spraying programme initiated in New Brunswick in 1951 has succeeded in minimizing tree mortality, but at the price of maintaining incipient outbreak conditions over an area considerably more extensive than in the past. The present management approach is, therefore, particularly sensitive to unexpected shifts in economic, social and regulatory constraints, and to unanticipated behavior of the forest ecosystem. Most major environmental problems in the world today are characterized by similar basic ingredients: high variability in space and time, large scale, and a troubled management history. Because of their enormous complexity there has been little concerted effort to apply systems analysis techniques to the coordinated development of effective descriptions of, and prescriptions for, such problems. The Budworm-forest system seemed to present an admirable focus for a case study with two objectives. The first, of course, was to attempt to develop sets of alternate policies appropriate for the specific problem. But the more significant purpose was to see just how far we could stretch the state of the art capabilities in ecology, modeling, optimization, policy design and evaluation to apply them to complex ecosystem management problems. Three principal issues in any resource environmental problem challenge existing techniques. The resources that provide the food, fibre and recreational opportunities for society are integral parts of ecosystems characterized by complex interrelationships of many species among each other and with the land, water and climate in which they live. The interactions of these systems are highly non-linear and have a significant spatial component. Events in anyone point in space, just as at any moment of time, can affect events at other points in space and time. The resulting high order of dimensionality becomes all the more significant as these ecological systems couple with complex social and economic ones. The second prime challenge is that we have only partial knowledge of the variables and relationships governing the systems. A large body of theoretical and experimental analysis and data has led to an identification of the general form and kind of functional relations existing between organisms. nut only occasionally is there a rich body of data specific to anyone situation. To develop an analysis which implicitly or explicitly presumes sufficient knowledge is therefore to guarantee management policies that become more the source of the problem than the source of the solution. In a particularly challenging way present ecological management situations require concepts and techniques which cope creatively with the uncertainties and unknowns that in fact pervade most of our major social, economic and environmental problems. The third and final challenge reflects the previous two: How can we design policies that achieve specific social objectives and yet are still "robust"? Policies which, once set in play, produce intelligently linked ecological, social and economic systems that can absorb the unexpected events and unknowns that will inevitably appear. These "unexpecteds" might be the one in a thousand year drought that perversely occurs this year; the appearance or disappearance of key species, the emergence of new economic and regulatory constrains or the shift of societal objectives. We must learn to design in a way which shifts our emphasis away from minimizing the probability of failure, towards minimizing the cost of those failures which will inevitably occur

Maximin and maximal solutions for linear programming problems with possibilistic uncertainty

We consider linear programming problems with uncertain constraint coefficients described by intervals or, more generally, possi-bility distributions. The uncertainty is given a behavioral interpretation using coherent lower previsions from the theory of imprecise probabilities. We give a meaning to the linear programming problems by reformulating them as decision problems under such imprecise-probabilistic uncer-tainty. We provide expressions for and illustrations of the maximin and maximal solutions of these decision problems and present computational approaches for dealing with them

Large-Scale Linear Programming

During the week of June 2-6, 1980, the System and Decision Sciences Area of the International Institute for Applied Systems Analysis organized a workshop on large-scale linear programming in collaboration with the Systems Optimization Laboratory (SOL) of Stanford University, and co-sponsored by the Mathematical Programming Society (MPS). The participants in the meeting were invited from amongst those who actively contribute to research in large-scale linear programming methodology (including development of algorithms and software). The first volume of the Proceedings contains five chapters. The first is an historical review by George B. Dantzig of his own and related research in time-staged linear programming problems. Chapter 2 contains five papers which address various techniques for exploiting sparsity and degeneracy in the now standard LU decomposition of the basis used with the simplex algorithm for standard (unstructured) problems. The six papers of Chapter 3 concern aspects of variants of the simplex method which take into account through basis factorization the specific block-angular structure of constraint matrices generated by dynamic and/or stochastic linear programs. In Chapter 4, five papers address extensions of the original Dantzig-Wolfe procedure for utilizing the structure of planning problems by decomposing the original LP into LP subproblems coordinated by a relatively simple LP master problem of a certain type. Chapter 5 contains four papers which constitute a mini-symposium on the now famous Shor-Khachian ellipsoidal method applied to both real and integer linear programs. The first chapter of Volume 2 contains three papers on non-simplex methods for linear programming. The remaining chapters of Volume 2 concern topics of present interest in the field. A bibliography a large-scale linear programming research completes Volume 2

Computational complexity of parametric linear programming

We establish that in the worst case, the computational effort required for solving a parametric linear program is not bounded above by a polynomial in the size of the problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47907/1/10107_2005_Article_BF01581642.pd