For a period starting in 2015, Apple, Google, and Microsoft became the most valuable companies in the world. Each was marked by an external developer ecosystem. Anecdotally, at least, developers matter. Using a formal model of code spillovers, we show how a rising number of developers can invert the firm. That is, firms will choose to innovate using open external contracts in preference to closed vertical integration. The locus of value creation moves from inside the firm to outside. Distinct from physical goods, digital goods a↵ord firms the chance to optimize spillovers. Further, firms that pursue high risk innovations with more developers can be more profitable than firms that pursue low risk innovations with fewer developers. More developers give platform firms more chances at success. Our contribution is to show why developers might cause a shift in organizational form and to provide a theory of how platform firms optimize their own intellectual property regimes in order to maximize growth. We use stylized facts from multiple platform firms to illustrate our theory and results.

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SUMMARYA non-parametric system identification-based model is presented for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy wavelet neural network (WNN) model developed by the authors. The model does not require complete measurements of the dynamic responses of the whole structure. A large structure is divided into a series of sub-structures around a few pre-selected floors where sensors are placed and measurements are made. The new model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively, thus resulting in fast training convergence and high accuracy. A new damage evaluation method is proposed based on a power density spectrum method, called pseudospectrum. The multiple signal classification (MUSIC) method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using the data obtained for a 38-storey concrete test model. The results demonstrate the effectiveness of the WNN model together with the pseudospectrum method for damage detection of highrise buildings based on a small amount of sensed data.

Recently, the authors presented a multiparadigm dynamic time-delay fuzzy wavelet neural network (WNN) model for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs. Compared with conventional neural networks, training of a dynamic neural network for system identification of large-scale structures is substantially more complicated and time consuming because both input and output of the network are not single valued but involve thousands of time steps. In this article, an adaptive Levenberg-Marquardt least-squares algorithm with a backtracking inexact linear search scheme is presented for training of the dynamic fuzzy WNN model. The approach avoids the second-order differentiation required in the Gauss-Newton algorithm and overcomes the numerical instabilities encountered in the steepest descent algorithm with improved learning convergence rate and high computational efficiency. The model is applied to two highrise moment-resisting building structures, taking into account their geometric nonlinearities. Validation results demonstrate that the new methodology provides an efficient and accurate tool for nonlinear system identification of high-rising buildings.