What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

Complex organizations exhibit surprising, nonlinear behavior. Although organization scientists have studied complex organizations for many years, a developing set of conceptual and computational tools makes possible new approaches to modeling nonlinear interactions within and between organizations. Complex adaptive system models represent a genuinely new way of simplifying the complex. They are characterized by four key elements: agents with schemata, self-organizing networks sustained by importing energy, coevolution to the edge of chaos, and system evolution based on recombination. New types of models that incorporate these elements will push organization science forward by merging empirical observation with computational agent-based simulation. Applying complex adaptive systems models to strategic management leads to an emphasis on building systems that can rapidly evolve effective adaptive solutions. Strategic direction of complex organizations consists of establishing and modifying environments within which effective, improvised, self-organized solutions can evolve. Managers influence strategic behavior by altering the fitness landscape for local agents and reconfiguring the organizational architecture within which agents adapt.

Complexity Theory and Organization Science

Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.

/pdf/adaptive-robust-optimization-for-the-security-constrained-vx9kggvs4i.pdf

Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

Researchers examining loosely coupled systems, knowledge management, and complementary practices in organizations have proposed, informally, that the complexity of a successful business strategy can deter imitation of the strategy. This paper explores this proposition rigorously. A simple model is developed that parametrizes the two aspects of strategic complexity: the number of elements in a strategy and the interactions among those elements. The model excludes conventional resource-based and game-theoretic barriers to imitation altogether. The model is used to show that complexity makes the search for an optimal strategy intractable in the technical sense of the word provided by the theory of NP-completeness. Consequently, would-be copycats must rely on search heuristics or on learning, not on algorithmic "solutions," to match the performance of superior firms. However, complexity also undermines heuristics and learning. In the face of complexity, firms that follow simple hill-climbing heuristics are quickly snared on low "local peaks," and firms that try to learn and mimic a high performer's entire strategy suffer large penalties from small errors. The model helps to explain why some winning strategies remain unmatched even though they are open to public scrutiny; why certain bundles of organizational practices diffuse slowly even though they lead to superior performance; and why some strategies yield superior returns even after many of their critical ingredients are adopted by competitors. The analysis also suggests roles for management science and managerial choice in a world of complex strategies.

Imitation of Complex Strategies

Nonlinear Programming: A Unified Approach

Can firms and coevolutionary groups suffer from too much interdependent complexity? Is complexity theory an alternative explanation to competitive selection for the emergent order apparent in coevolutionary industry groups? The biologist Stewart Kauffman suggests a theory of complexity catastrophe offering universal principles explaining phenomena normally attributed to Darwinian natural selection theory. Kauffman's complexity theory seems to apply equally well to firms in coevolutionary pockets. Based on complexity theory, four kinds of complexity are identified. Kauffman's "NK[C] model" is positioned "at the edge of chaos" between complexity driven by "Newtonian" simple rules and rule-driven deterministic chaos. Kauffman's insight, which is the basis of the findings in this paper, is that complexity is both a consequence and a cause. Multicoevolutionary complexity in firms is defined by moving natural selection processes inside firms and down to a "parts" level of analysis, in this instance Porter's value chain level, to focus on microstate activities by agents. The assumptions of stochastically idiosyncratic microstates and coevolution in firms are analyzed. Competitive advantage, as a dependent variable, is defined in terms of Nash equilibrium fitness levels. This allows a translation of Kauffman's theory to firms, paying particular attention to (1) how value chain landscapes might be modeled, (2) assumptions underlying Kauffman's models making them amenable to firms, and (3) a delineation of seven of Kauffman's computational experiments. As part of the translation, possible parallels between the application of complexity catastrophe theory to coevolutionary pockets and studies by institutional theorists and social network analysts are discussed. The models derive from spin-glass microstate models resulting in Boolean games. Kauffman's Boolean statistical mechanics is introduced in developing the logic underlying the somewhat simplified NKM[C] model. The model allows the use of computational experiments to better understand how the dependent variable-value chain fitness-is affected by changes in the number of internal interdependencies K, the number of coevolutionary links with opponents C, the size of the coevolutionary pocket S, and the number of simultaneous adaptive changes, among other things. Various computational experiments are presented that suggest strategic organizing approaches most likely to foster competitive advantage. High or low Nash equilibrium fitness levels are shown to result from internal and external coevolutionary densities as a function of links among value chain competencies within a firm and between a firm and an opponent. Complexity phenomena appear to suggest a number of expected (and thus validating) and surprising strategies with respect to complex organizational interdependencies. For example, moderate complexity fares best and external coevolutionary complexity sets an upper bound to advantages likely to be gained from internal complexity. Various complexity "lessons" are discussed. Models such as the NK[C] could offer insights into strategic organizing.

https://www.jstor.org/stable/info/2640333

Avoiding Complexity Catastrophe in Coevolutionary Pockets: Strategies for Rugged Landscapes

This paper develops a solution method for scheduling units of a power-generating system to produce electricity by taking into consideration the stochasticity of the hourly load and its correlation structure. The unit commitment problem is initially formulated as a chance constrained optimization problem in which we require that the load be met with a specified high probability over the entire time horizon. The solution procedure consists of solving a sequence of deterministic versions of the unit commitment problem that converge to the solution of the chance constrained program. For the deterministic unit commitment problems, Lagrangian relaxation is used to separate the dual problem into its subproblems. Each subproblem is solved by a dynamic program. The initial results indicate that accounting for the correlation structure of the hourly loads reduces the value of the objective function when the optimization problem is formulated as a chance constrained program. Monte Carlo simulation is used to verify the accuracy of the solution provided by the algorithm. The relationship that the unit commitment solution found using the chance constrained optimization approach has with that found using conventional spinning reserves is discussed.

A solution to the stochastic unit commitment problem using chance constrained programming

Summary form only given. The paper develops a solution method for scheduling units of a power-generating system to produce electricity by taking into consideration the stochasticity of the hourly load and its correlation structure. The unit commitment problem is initially formulated as a chance-constrained optimization problem in which we require that the load be met with a specified high probability over the entire time horizon. The solution procedure consists of solving a sequence of deterministic versions of the unit commitment problem that converge to the solution of the chance constrained program. For the deterministic unit commitment problems, Lagrangian relaxation is used to separate the dual problem into its subproblems. Each subproblem is solved by a dynamic program. The initial results indicate that accounting for the correlation structure of the hourly loads reduces the value of the objective function when the optimization problem is formulated as a chance constrained program. Monte Carlo simulation is used to verify the accuracy of the solution provided by the algorithm. The relationship that the unit commitment solution found using the chance constrained optimization approach has with that found using conventional optimization spinning reserves is discussed.

A fundamental bid-based stochastic model is presented to predict electricity hourly prices and average price in a given period. The model captures both the economic and physical aspects of the pricing process, considering two sources of uncertainty: availability of the units and demand. This work is based on three oligopoly models-Bertrand, Cournot, and supply function equilibrium (SFE) due to Rudkevich, Duckworth, and Rosen-and obtains closed form expressions for expected value and variance of electricity hourly prices and average price. Sensitivity analysis is performed on the number of firms, anticipated peak demand, and price elasticity of demand. The results show that as the number of firms in the market decreases, the expected values of prices increase by a significant amount. Variances for the Cournot model also increase, but the variances for the SFE model decrease, taking even smaller values than Bertrand's. Thus, if the Rudkevich model is an accurate representation of the electricity market, the results show that an introduction of competition may decrease the expected value of prices but the variances may actually increase. Finally, using a refinement of the model, it has been demonstrated that an accurate temperature forecast can reduce significantly the prediction error of the electricity prices.

http://quantlabs.net/academy/download/free_quant_instituitional_books_/[Universidad%20de%20Montevideo,%20Ruibal]%20Forecasting%20the%20Mean%20and%20the%20Variance%20of%20Electricity%20Prices%20in%20Deregulated%20Markets.pdf

Forecasting the Mean and the Variance of Electricity Prices in Deregulated Markets

This paper highlights the need for considering the stochastic processes associated with the frequency and duration of generating unit outages for assessing the mean and variance of production costs under operating constraints. A numerical example based on a Markov model is given to show that Monte Carlo estimates of these quantities may be incorrect if only the forced outage rates are used in place of the stochastic parameters underlying the outage frequency and duration. Additionally it describes a variance reduction procedure whereby the Monte Carlo estimates can be obtained with a much smaller sample size than would be required otherwise. A numerical example is given for a small system.

Mainak Mazumdar

Papers

Avoiding Complexity Catastrophe in Coevolutionary Pockets: Strategies for Rugged Landscapes

A solution to the stochastic unit commitment problem using chance constrained programming

A solution to the stochastic unit commitment problem using chance constrained programming

Forecasting the Mean and the Variance of Electricity Prices in Deregulated Markets

Monte Carlo Computation of Power Generation Production Costs under Operating Constraints