State-Dependent Choice Model for TV Programs with Externality: Analysis of Viewing Behavior

In this paper we develop a general class of dynamic brand choice models, called Lightning Bolt (LB) models, which are consistent with the theory of random utility maximization of consumer choice behavior. The underlying random utility process is Markov, and the inter-temporal evolution of the (utility-maximizing) brand choice process is also Markov. The models permit parsimonious parameterizations of the random utility process in brand choices with the resulting switching probabilities being functionally related to explanatory variables. The model allows for structural state dependence (feedback), habit persistence (inertia), and unobserved heterogeneity. The theoretical development shows that several well known stochastic brand choice models can be deduced from random utility maximization theory. From a managerial perspective, the usefulness of the proposed model stems from its ability to separate out the effects of habits, state dependence and heterogeneity. Strong state dependence effects imply incentives for inducing trial of a brand (e.g., product sampling). In contrast with state dependence, a strong habit persistence effect may be indicative of buyer behavior where inducement of trial of a different brand may not be sufficient to maintain a (sustained) defection of the consumer from the habitually purchased brand to the trial brand. Failure to distinguish between these two effects has important implications. For example, a model that only accounts for state dependence effects would, in the presence of only habit persistence, incorrectly attribute it to state dependence. Based on this the manager could decide to embark on an expensive sampling program that might prove ineffective due to the absence of state dependence. It is also important to distinguish between the effects of unobserved heterogeneity and state dependence. In the absence of true state dependence, failing to account for unobservable variations across households (such as differences in price sensitivities), results in the temporally persistent unobservable elements showing up as state dependence in the model. Hence, a manager may incorrectly opt for sampling as the appropriate marketing action, whereas, a couponing or price promotion strategy should have been preferred. We estimate the model parameters using the AC Nielsen household scanner panel data set on catsup purchases. Further, we investigate empirical techniques for overcoming the “initial conditions” problem that affects many dynamic models of brand choice. Through simulation analysis using the estimated parameters, we show that the calculated profitability of a promotion must take into account the multi-period impact due to state dependence. Further, we demonstrate how ignoring heterogeneity can result in spurious state dependence, thereby making marketing tactics such as product sampling appear far more attractive than they actually are. Specifically, the model that accounts only for habit persistence and state dependence effects predicts a share increase of 2.4% points through sampling for one of the brands in the empirical analysis. Once the effects of unobserved heterogeneity are accounted for, however, this number drops precipitously to 0.3%. While even this low number might fulfill the objectives of the brand manager, it ensures that expectations are not eight times that number. It is important to note that this paper is a first attempt at analyzing the three fundamental dimensions of dynamic brand choice behavior. Our formulation represents a “reduced form” approach as opposed to a “structural” approach. Other limitations of the model include our focus on time-invariant choice sets. Extensions of the model to situations where choice sets vary over time are possible, but are difficult. Another limitation is the dependence on multivariate extreme value distributions. An alternative would be to use multivariate normal distributions, i.e., a probit-like structure which has certain useful properties. A comparison of the two approaches would be useful. Empirical extensions of the model to allow for higher-order processes and nested logit type model structures could also be fruitful. Explicitly incorporating variety-seeking behavior in the model would further enrich the theoretical framework proposed in this paper.

Rats with cannulas implanted in the septal area were conditioned, tested, or both conditioned and tested shortly after intracerebral injection of local anesthetic via the cannulas. A 2 X 2 factorial design was used to determine whether the presumed state of temporary septal area dysfunction, previously shown to produce amnesia, has state-dependent properties. A state-dependent learning effect was observed in the rats both conditioned and tested in the dysfunctional stat remembered the aversive conditioning better than those conditioned in the dysfunctional state but tested in the normal state. Since rats conditioned in the normal state but tested during septal dysfunction did appear to exhibit conditioned fear when tested, the state-dependent effect was asymmetrical. Performance effects of the procaine injection were observed and accounted for in determining the state-dependent nature of temporary septal area dysfunction.

Probabilistic versus random-utility models of state dependence: an empirical comparison

Since FY 2013, as a part of the Affordable Care Act (ACA) program, the Hospital Value-Based Purchasing (HVBP) program has adjusted Medicare’s payments to hospitals based on the total performance score of the hospital. First, the program reduces a portion of the hospital’s Medicare payments in a specific fiscal year, and then, by the end of the same fiscal year, the amount of the payment reductions will be awarded to the hospitals based on the total performance score; thus, the hospitals that do not receive the reward will lose the portion of money reduced by Medicare. In this research, we apply the theory of state dependence and use the dynamic random effect probit model to estimate this effect. The results show that the hospital payment adjustment dynamics have a very significant state dependence effect (0.341); this means that hospitals that received a reward in the previous year are 34.1% more likely to receive a reward this year than the ones that received a penalty in the previous year. Meanwhile, we also find that the state dependence effect varies significantly across hospitals with different ownership (proprietary/government owned/voluntary nonprofit), and the results show that voluntary nonprofit hospitals exhibit the largest effect of state dependence (0.370), while government-owned hospitals exhibit the lowest effect of state dependence (0.293), and proprietary hospitals are in the middle. Among the factors that influence the likelihood that a hospital receives a reward, we find that teaching hospitals with a large number of beds (>400) are less likely be rewarded; in terms of ownership, we find that voluntary nonprofit hospitals are more likely be rewarded; in terms of demographic factors, hospitals where the average household income are higher within the region are more likely be rewarded.

Emerging experimental evidence suggests that both networks and their component neurons respond to similar inputs differently depending on the state of network activity. The network state is determined by the intrinsic dynamical structure of the network and may change as a function of neuromodulation, the balance or stochasticity of synaptic inputs to the network and the history of network activity. Much of the knowledge on state-dependent effects comes from comparisons of awake and sleep states of the mammalian brain. Yet, the mechanisms underlying these states are difficult to unravel. Several vertebrate and invertebrate studies have elucidated cellular and synaptic mechanisms of state-dependence resulting from neuromodulation, sensory input, and experience. Recent studies have combined modeling and experiments to examine the computational principles that emerge when network state is taken into account; these studies are highlighted in this article. We discuss these principles in a variety of systems (mammalian, crustacean, and mollusk) to demonstrate the unifying theme of state-dependence of network output.

No prior investigation has considered the effects of state dependence and unobserved heterogeneity on the relationship between regular physical activity (RPA) and latent health stock (LHS). Accounting for state dependence corrects the possible overestimation of the impact of socioeconomic factors. We estimated the degree of the state dependence of RPA and LHS among middle-aged Japanese workers. The 5 years’ longitudinal data used in this study were taken from the Longitudinal Survey of Middle and Elderly Persons. Individual heterogeneity was found for both RPA and LHS, and the dynamic random-effects probit model provided the best specification. A smoking habit, low educational attainment, longer work hours, and longer commuting time had negative effects on RPA participation. RPA had positive effects on LHS, taking into consideration the possibility of confounding with other lifestyle variables. The degree of state dependence of LHS was positive and significant. Increasing the intensity of RPA had positive effects on LHS and caused individuals with RPA to exhibit greater persistence of LHS compared to individuals without RPA. This result implies that policy interventions that promote RPA, such as smoking cessation, have lasting consequences. We concluded that smoking cessation is an important health policy to increase both the participation in RPA and LHS.

Life cycle employment and fertility across institutional environments

This study investigates discrepancies in offset choices between real-world and hypothetical scenarios by comparing stated preference (SP) and self-reported revealed preference (RP) data. Using multinomial logit (MNL) and mixed logit (MXL) models, key findings are: (1) preferences for offset contributions and prices are consistent across SP and RP, but differ in project types, locations, and certifications. (2) partial data enrichment harmonizes SP and RP within MNL models, outperforming full enrichment. (3) positive state dependence shows previous choices influence stated ones, with individual heterogeneity in MXL models. (4) incorporating unobserved heterogeneity and state-dependence effects is crucial when merging SP and RP.

Focus on Authors

TV audience rating is an important indicator regarding the popularity of programs and it is also a factor to influence the revenue of broadcast stations via advertisements. Presently, the only way for assessing audience rating is the Nielsen TV rating, which depends on a small number of randomly selected representative groups, because of practical considerations such as cost and survey time. The way to obtain audience rating is using 'People-meter' which is a device installed in user's house and regularly records the rating surveys. However, we are not able to know the audience rating immediately since sometimes we have to make a marketing decision and lack of indicator. Currently, the present media environments are drastically changing our media consumption patterns. We can watch TV programs on Youtube regardless location and timing. And Nielsen TV audience rating does not take the social networking site into account. In this paper, we develop a model for predicting TV audience rating. We accumulate the broadcasted TV programs' word-of-mouse on Facebook and apply the Back-propagation Network to predict the latest program audience rating. We also present the audience rating trend analysis on demo system which is used to describe the relation between predictive audience rating and Nielsen TV rating.

We investigate the factors underlying poverty transitions in Nairobi’s slums focusing on whether differences in characteristics make people more prone to enter poverty and persist in, or whether past experience of poverty matters on future states. Understanding these issues is essential for the design of effective policy programs aimed at enhancing the lives of the poor. The paper uses an endogenous switching model, which accounts for initial conditions, non-random attrition, and unobserved heterogeneity.Estimations are based on a panel dataset from the Nairobi Demographic Surveillance System. Results indicate that true state dependence (TSD) constitutes the major factor driving poverty persistence. There are little heterogeneity effects. Even when household and individual observed characteristics differ notably, the TSD size remains very large. Active anti-poverty programs aimed at breaking the cycle of poverty constitute then the most appropriate policies for taking people out of poverty and preventing them to fall back in.

Estrous cycle contributes to state-dependent contextual fear in female rats

A state dependence model of serial behavior suggests that each occurrence increases the subsequent likelihood of that behavior being repeated. A heterogeneity model, by contrast, suggests that the likelihood of a behavior occurring is predetermined, and uninfluenced by intervening occurrences. We have applied the random-effects probit model of Gibbons and Bock (1987) to examine the fit of the state dependence and heterogeneity models to longitudinal data on suicide attempts by 928 patients with affective disorder. Heterogeneity but not state dependence was required to model these data. The findings suggest that when considering patients with moderate to severe major affective disorder, the clinician should not interpret the absence of any recent suicide attempts to mean that the patient is at relatively low risk for attempting suicide in the future. An implication of the heterogeneity model is that suicide attempts made many years ago may have equal value to recent attempts when estimating an individual's "predisposition" to nonlethal attempts in the future.

A Non-Bayesian Theory of State-Dependent Utility

Drugs and human memory (part 2). Clinical, theoretical, and methodologic issues.