Multi-Type Consumer Interactions under Local Network Externality

Author(s):  
Arnut Paothong ◽  
G.S. Ladde
Author(s):  
Saed Alizamir ◽  
Ningyuan Chen ◽  
Sang-Hyun Kim ◽  
Vahideh Manshadi

We analyze a firm’s optimal pricing of a new service when consumers interact in a network and impose positive externality on one another. The firm initially provides its service for free, leveraging network externality to promote rapid service consumption growth. The firm raises the price and starts earning revenue only when a sufficient level of consumer interactions is established. Incorporating the local network effects in a nonstationary dynamic model, we study the impact of network structure on the firm’s revenue and optimal pricing decision. We find that the firm delays the timing of service monetization when it faces a more strongly connected network despite the fact that such a network allows the firm to monetize the service sooner by resulting in faster consumption growth. We also find that the firm benefits from network imbalance; that is, the firm prefers a network of consumers with varying degrees of connections to that with similar degrees of connections. We also study the value of knowing the network structure and discuss how such knowledge impacts the firm’s profitability. Our analyses rely on the techniques from algebraic graph theory, which enable us to solve the firm’s high-dimensional dynamic pricing problem by relating it to the network’s spectral characteristics.


2002 ◽  
Vol 122 (2) ◽  
pp. 267-274 ◽  
Author(s):  
Kenji Okuyama ◽  
Takeyoshi Kato ◽  
Kai Wu ◽  
Yasunobu Yokomizu ◽  
Tatsuki Okamoto ◽  
...  

Author(s):  
Arie W. Kruglanski ◽  
Jocelyn J. Bélanger ◽  
Rohan Gunaratna

This book identifies the three major determinants of radicalization that progresses into violent extremism, the three Ns of radicalization. The first determinant is the need: Individuals’ universal desire for personal significance. The second determinant is the narrative. Because significance is conferred by members of one’s group, the group’s narrative guides members in their quest for significance. The third determinant is the network: membership of one’s group who validate the narrative and who dispense rewards (respect and veneration) to members who implement it. The quest for significance is activated in one of three major ways: (a) through a loss of significance occasioned by personal failure or affront to one’s social identity (e.g., ethnicity, religion, race), (b) through a threat of significance loss if one failed to respond to a challenge or to defend one’s group values, and/or (c) through an opportunity for a significance gain (e.g., becoming a hero or a martyr) by selflessly defending one’s group values. In groups that see their values (e.g., religion, sovereignty, culture) under threat from some (real or imagined) actor, the narrative often justifies violence against the detractor and portrays it as a supreme road to significance. Especially where violence is contrary to the norms of the mainstream society, validation of the violence–significance link by the local network is particularly important. The present 3N model of radicalization and the varied empirical evidence that supports it are leveraged to interpret prior theories of radicalization and to address major issues in the domains of deradicalization and recidivism.


Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 954 ◽  
Author(s):  
Hanne Kauko ◽  
Daniel Rohde ◽  
Armin Hafner

District heating enables an economical use of energy sources that would otherwise be wasted to cover the heating demands of buildings in urban areas. For efficient utilization of local waste heat and renewable heat sources, low distribution temperatures are of crucial importance. This study evaluates a local heating network being planned for a new building area in Trondheim, Norway, with waste heat available from a nearby ice skating rink. Two alternative supply temperature levels have been evaluated with dynamic simulations: low temperature (40 °C), with direct utilization of waste heat and decentralized domestic hot water (DHW) production using heat pumps; and medium temperature (70 °C), applying a centralized heat pump to lift the temperature of the waste heat. The local network will be connected to the primary district heating network to cover the remaining heat demand. The simulation results show that with a medium temperature supply, the peak power demand is up to three times higher than with a low temperature supply. This results from the fact that the centralized heat pump lifts the temperature for the entire network, including space and DHW heating demands. With a low temperature supply, heat pumps are applied only for DHW production, which enables a low and even electricity demand. On the other hand, with a low temperature supply, the district heating demand is high in the wintertime, in particular if the waste heat temperature is low. The choice of a suitable supply temperature level for a local heating network is hence strongly dependent on the temperature of the available waste heat, but also on the costs and emissions related to the production of district heating and electricity in the different seasons.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.


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