Query: Diffusion Decision Model economics

title: Are Economics and Psychology Complements in Household Technology Diffusion? Evidence from a Natural Field Experiment
abstract: This paper uses a field experiment to estimate the effects of prices and social norms on the decision to adopt and efficient technology. We find that prices and social norms influence the adoption and decision along different margins: while prices operate on both the extensive and intensive margins, social norms operate mostly through the extensive margin. This has both positive and normative implications, and suggests that economics and psychology may be strong complements in the diffusion process. To complement the reduced form results, we estimate a structural model that points to important household heterogeneity: whereas some consumers welcome the opportunity to purchase and learn about the new technology, for others the inconvenience and social pressure of the ask results in negative welfare. As a whole, our findings highlight that the design of optimal technological diffusion policies will require multiple instruments and a recognition of household heterogeneity.

title: Singular dividend optimization for a linear diffusion model with time inconsistent preferences
abstract: With the advancement of behavioral economics, the use of exponential discounting for decision making in neoclassical economics has been questioned since it cannot provide a realistic way to explain certain decision making behavior.The purpose of this paper is to investigate strategic decision making on dividend distribution policies of insurance companies when the management adopts a more realistic way for discounting, namely stochastic quasi hyperbolic discounting. The use of this more realistic way for discounting is motivated by some recent developments in behavioral economics. A game theoretic approach is adopted to establish economic equilibrium results, namely subgame perfect Markov equilibrium strategies. It is shown that (1) under certain mild technical conditions, the barrier strategy with an optimal barrier, which is widely used in the traditional approach to optimal dividend problems, is a perfect Markov equilibrium strategy, (2) the optimal barrier is lower than the barrier of an optimal strategy obtained from the respective time consistent optimal dividend problem, and (3) the solution based on the barrier strategy does not exist in some situations.

title: Improving Out of Sample Predictions Using Response Times and a Model of the Decision Process
abstract: A basic problem in empirical economics involves using data from one domain to make out of sample predictions for a different, but related environment. When the choice data are binary, a canonical method for making these types of predictions is the logistic choice model. This paper investigates whether it is possible to improve out of sample predictions by changing two aspects of the canonical approach: 1) Using response times in addition to the choice data, and 2) Combining them using a model from the psychology and neuroscience literature, the Drift Diffusion Model (DDM) Two experiments compare the out of sample choice prediction accuracies of both methods and in both cases the DDM method outperforms a logistic prediction method. Furthermore, the DDM allows for out of sample process predictions. Both experiments validate the DDM as a method for predicting out of sample response times.

title: North South diffusion of climate mitigation technologies: the crowding out effect on relocation
abstract: The deployment of cleaner production technologies is supposed to be crucial to mitigate the effect of climate change. The diffusion of technology from developed to developing countries can be done through different channels. It can be a business decision such as firms' relocation, opening of a subsidiary or the adoption of technology by southern firms, or it may be decided at the government level. This paper investigates, in a two country model (North and South) the relationship between the diffusion of mitigation technologies, firms' relocation and the environment. We assume that both countries implement a carbon tax and there are two kinds of production technology used: a relatively clean technology and a dirty one. This paper theoretically shows that the technology diffusion by technology adoption, public transfer or subsidiary creation induces a decrease in relocation, while technology diffusion via purchasing dirty southern firms may increase the number of relocated firms. The paper also demonstrates that technology diffusion may have perverse effects in the long run. Indeed, total emissions may increase with technology diffusion since southern firms become more competitive.

title: Improving Out of Sample Predictions Using Response Times and a Model of the Decision Process
abstract: A basic problem in empirical economics involves using data from one domain to make out of sample predictions for a different, but related environment. When the choice data are binary, a canonical method for making these types of predictions is the logistic choice model. This paper investigates whether it is possible to improve out of sample predictions by changing two aspects of the canonical approach: 1) Using response times in addition to the choice data, and 2) Combining them using a model from the psychology and neuroscience literature, the Drift Diffusion Model (DDM) Two experiments compare the out of sample choice prediction accuracies of both methods and in both cases the DDM method outperforms a logistic prediction method. Furthermore, the DDM allows for out of sample process predictions. Both experiments validate the DDM as a method for predicting out of sample response times.

title: Agent Based Modeling of Feeder Level Electric Vehicle Diffusion for Distribution Planning
abstract: Faced with a rapidly growing electric vehicle (EV) load, distribution planners need a methodology to forecast when and where these loads will likely appear on specific distribution feeders. This paper proposes a new diffusion forecasting approach for residential EV and charging stations using agent based modeling. Residential EV adoption is treated as a multi criteria decision making problem modeled via analytic hierarchy process (AHP) The customer adoption model CANE is developed considering customer characteristics including car age, EV attractiveness valued using logistic regression, neighbor influences and customer economics. Distribution feeder topology is combined with property geographic information system (GIS) parcels and household travel survey data to determine geographic and electric locations for EV and charging stations. A Monte Carlo method is utilized to obtain the most likely outcome of the stochastic vehicle decision making process and perform model calibration. The proposed diffusion forecasting approach is demonstrated using actual distribution feeder data. Using the diffusion results, EV impact on system annual peak, energy, losses, transformer aging and feeder upgrades is evaluated using quasi static time series power flow analysis. Case analyses are presented that examine the effect of EV price and charging station placement on EV diffusion and distribution feeder impact.

title: The Behavioralist Goes Door to Door: Understanding Household Technological Diffusion Using a Theory Driven Natural Field Experiment
abstract: This paper uses a field experiment to estimate behavioral parameters from a structural model of residential adoption of technology. As our model includes both economic and psychological factors, we are able to identify the role of prices, social norms, social pressure, and curiosity on the adoption decision. We find that prices and social norms influence the adoption decision along different margins, opening up the opportunity for economics and psychology to be strong complements in the diffusion process. In addition, welfare estimates from our structural model point to important household heterogeneities: whereas some consumers welcome the opportunity to purchase and learn about the new technology, for others the inconvenience and social pressure of the ask results in negative welfare. As a whole, our findings highlight that the design of optimal technological diffusion policies will require multiple instruments and a recognition of individual household heterogeneities.

title: The Behavioralist Goes Door To Door: Understanding Household Technological Diffusion Using a Theory Driven Natural Field Experiment
abstract: This paper uses a field experiment to estimate behavioral parameters from a structural model of residential adoption of technology. As our model includes both economic and psychological factors, we are able to identify the role of prices, social norms, social pressure, and curiosity on the adoption decision. We find that prices and social norms influence the adoption decision along different margins, opening up the opportunity for economics and psychology to be strong complements in the diffusion process. In addition, welfare estimates from our structural model point to important household heterogeneities: whereas some consumers welcome the opportunity to purchase and learn about the new technology, for others the inconvenience and social pressure of the ask results in negative welfare. As a whole, our findings highlight that the design of optimal technological diffusion policies will require multiple instruments and a recognition of individual household heterogeneities.

title: Response times in economics: Looking through the lens of sequential sampling models
abstract: Economics is increasingly using process data to make novel inferences about preferences and predictions of choices. The measurement of response time (RT) the amount of time it takes to make a decision, offers a cost effective and direct way to study the choice process. Yet, relatively little theory exists to guide the integration of RT into economic analysis. This article presents a canonical process model from psychology and neuroscience, the Drift Diffusion Model (DDM) and shows that many RT phenomena in the economics literature are consistent with the predictions of the DDM. Additionally, use of the class of sequential sampling models facilitates a more principled consideration of findings from cognitive science and neuroeconomics. An application of the DDM to a binary choice dataset demonstrates the rich inference made possible when using models that can jointly model choice and process, highlighting the need for more work in this area.

title: The potential and economics of EV smart charging: A case study in Shanghai
abstract: The development of electric vehicles (EVs) is not only essential to improving energy efficiency and reducing emissions in transportation sector, but also increases power system flexibility through demand response. However, the quantitative research on the potential and economics of smart charging for different types of EVs in China remains insufficient, which has been increasingly regarded as a critical barrier for associated policy decision marking. To address this issue, this paper conducts a survey on the charging behaviors of four types of EVs in Shanghai and evaluates the capacity potential and cost of EV smart charging. A Bass Diffusion model is adopted to forecast the stock growth of EVs and the potential of smart charging is investigated by simulating EV charging behavior under two scenarios. It finds that private and commercial EVs could deliver greater smart charging than that of electric bus and taxis. Business models and charging policies that could cost effectively explore the potential of EV smart charging are further discussed. The paper concludes that charging service providers with time of use charging tariffs could realize the potential of smart charging in a cost efficient way.