Leon Recanati

Bio: Leon Recanati is an academic researcher. The author has contributed to research in topics: Stochastic cellular automaton & Aggregate data. The author has an hindex of 1, co-authored 1 publications receiving 295 citations.

Using Complex Systems Analysis to Advance Marketing Theory Development: Modeling Heterogeneity Effects on New Product Growth through Stochastic Cellular Automata

Abstract: Aggregate level simulation procedures have been used in many areas of marketing. In this paper we show how individual level simulations may be used support marketing theory development. More specifically, we show how a certain type of simulations that is based on complex systems studies (in this case Stochastic Cellular Automata) may be used to generalize diffusion theory one of the fundamental theories of new product marketing. Cellular Automata models are simulations of global consequences, based on local interactions between individual members of a population, that are widely used in complex system analysis across disciplines. In this study we demonstrate how the Cellular Automata approach can help untangle complex marketing research problems. Specifically, we address two major issues facing current theory of innovation diffusion: The first is general lack of data at the individual level, while the second is the resultant inability of marketing researchers to empirically validate the main assumptions used in the aggregate models of innovation diffusion. Using a computer-based Cellular Automata Diffusion Simulation, we demonstrate how such problems can be overcome. More specifically, we show that relaxing the commonly used assumption of homogeneity in the consumers’ communication behavior is not a barrier to aggregate modeling. Thus we show that notwithstanding some exceptions, the well-known Bass model performs well on aggregate data when the assumption that that all adopters have a possible equal effect on all other potential adopters is relaxed. Through Cellular Automata we are better able to understand how individual level assumptions influence aggregate level parameter values, and learn the strengths and limitations of the aggregate level analysis. We believe that this study can serve as a demonstration towards a much wider use of Cellular Automata models for complex marketing research phenomena.

Maximizing the spread of influence through a social network

Abstract: Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

Learning influence probabilities in social networks

Abstract: Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.

Influential nodes in a diffusion model for social networks

Abstract: We study the problem of maximizing the expected spread of an innovation or behavior within a social network, in the presence of “word-of-mouth” referral. Our work builds on the observation that individuals’ decisions to purchase a product or adopt an innovation are strongly influenced by recommendations from their friends and acquaintances. Understanding and leveraging this influence may thus lead to a much larger spread of the innovation than the traditional view of marketing to individuals in isolation. In this paper, we define a natural and general model of influence propagation that we term the decreasing cascade model, generalizing models used in the sociology and economics communities. In this model, as in related ones, a behavior spreads in a cascading fashion according to a probabilistic rule, beginning with a set of initially “active” nodes. We study the target set selection problem: we wish to choose a set of individuals to target for initial activation, such that the cascade beginning with this active set is as large as possible in expectation. We show that in the decreasing cascade model, a natural greedy algorithm is a 1-1/ e-e approximation for selecting a target set of size k.