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Using two-sided markets as a specific market context, we show that cross-group network effects can turn the participants of a two-sided network into critical resources. In two-sided markets such as payment cards and personal computer operating systems, two groups of agents interact with each other via a common network platform; the value of joining the network for agents in one group depends on the number of participants in the other group. In these markets, resource heterogeneity is represented by different sizes of existing networks; resource accumulation possesses all five characteristics of asset-stock accumulation summarized by Dierickx and Cool. The unique resource accumulation process provides an isolating mechanism for large networks to sustain their resource and competitive advantages. Using two dynamic systems models, we show that resource heterogeneity (i.e. varying initial network sizes) is a source of sustained competitive advantage for two-sided networks and has significant impact on long-term competition dynamics. The findings illustrate the importance of incorporating market context in the research of the resource-based view of competitive advantage. Copyright (c) Blackwell Publishing Ltd 2009.

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We study the diffusion of competing two-sided networks using a differential game framework. We find that whether the winner takes all depends on the participation behaviour of individual agents on both sides of the market. When individual agents tend to participate in only one network, one network will dominate the market. As the tendency for joining multiple networks increases, the possibility for two networks to co-exist in the long-run also increases. Thus the steady-state market share of competing two-sided networks can be very different depending on adopters' choice of "multi-homing" or "single-homing".

A new method is presented for controlling a discrete-the linear system with, possibly time-varying, random parameters in the presence of input and output noise. The cost is assuned to be quadratic in the state and control.Previous algorithms for the above problem when the system had both zeroes and poles unknown were of the open-loop feedback type, i.e., they did not take into account that future observations will be made. Therefore, even though these schemes were adaptive, their learning was "accidental". In contrast to this, the new approach uses an expression of the optimal costto-go that exhibits the dual purpose of the control: learning and control.The effect of the present control on the future estlmation ("learning") appears explicitly in the cost used in the stochastic dynamic programdug equation. The resulting sequence of controls, which is of the closed-loop type, is Shawn via simulations to appropriately divide its energy between the learning and the control purposes. Therefore, this control is called actively adaptive because it regulates the speed and BmOunt of learning as required by the performance index. The simulations on a third order system with six unknown parameters also demonstrate the computational feasibility of the proposed algorithm.