State Dependence of Network Output: Modeling and Experiments

This study analyzes TV program content for viewers with respect to the following three properties: primary utility (intrinsic value by genre, cast, and so on), macro externality, such as the effect of others' past behavior (audience ratings), and state dependence, or the viewer's past actions that determine whether to watch the program. Focusing on the effects of macro externality and state-dependence on viewer behavior, the authors construct a hierarchical Bayesian model and derive an individual utility function. Applying their method to the choice of TV programs in Japan, they estimate the effects of macro externality and state dependence on an individual's viewing behavior, and illustrate the distribution of these effects. The authors find that macro externality has a strong overall effect on the total number of viewers and the state-dependence effect has a slightly positive effect. Moreover, comparing the attributes revealed that male viewers tend to show a lower effect of ratings and higher state dependence than females. They find that the older the viewers are, the higher the effect of state dependence. Further, using utility function data obtained by our estimation, they conduct a simulation to measure the dynamic effects of macro externality and state dependence.

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.

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.

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

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.

Watching a paradigm shift in neuroscience

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

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.

Air-stepping can be used as a model for investigating rhythmogenesis and its interaction with sensory input. Here we show that it is possible to entrain involuntary rhythmic movement patterns in healthy humans by using different kinds of stimulation techniques. The subjects lay on their sides with one or both legs suspended, allowing low-friction horizontal rotation of the limb joints. To evoke involuntary stepping of the suspended leg, either we used continuous muscle vibration, electrical stimulation of the superficial peroneal or sural nerves, the Jendrassik maneuver, or we exploited the postcontraction state of neuronal networks (Kohnstamm phenomenon). The common feature across all stimulations was that they were tonic. Air-stepping could be elicited by most techniques in about 50% of subjects and involved prominent movements at the hip and the knee joint (approximately 40-70 degrees). Typically, however, the ankle joint was not involved. Minimal loading forces (4-25 N) applied constantly to the sole (using a long elastic cord) induced noticeable (approximately 5-20 degrees) ankle-joint-angle movements. The aftereffect of a voluntary long-lasting (30-s) contraction in the leg muscles featured alternating rhythmic leg movements that lasted for about 20-40 s, corresponding roughly to a typical duration of the postcontraction activity in static conditions. The Jendrassik maneuver per se did not evoke air-stepping. Nevertheless, it significantly prolonged rhythmic leg movements initiated manually by an experimenter or by a short (5-s) period of muscle vibration. Air-stepping of one leg could be evoked in both forward and backward directions with frequent spontaneous transitions, whereas involuntary alternating two-legged movements were more stable (no transitions). The hypothetical role of tonic influences, contact forces, and bilateral coordination in rhythmogenesis is discussed. The results overall demonstrated that nonspecific tonic drive may cause air-stepping and the characteristics and stability of the evoked pattern depended on the sensory input.

A key step toward understanding how the brain works is the reliable characterization of directed functional (i.e., causal) connectivity between brain regions, generally during various states of wake and sleep, as well as during task performance. Here we perform a critical analysis of the potential of Granger causality (GC) [1] to confront this challenge. We describe new methodology for rigorously applying GC to steady-state data, validated against analytical calculations, and for the first time, simulated spiking neuron local field potential data. We apply our methods to scalp and intracranial electroencephalographic (EEG) recordings. The concept of GC is to quantify the extent to which the past of one signal Y predicts the future of another signal X. For 'pairwise' GC, one compares predictions based on the past of just × and on the past of both × and Y. For 'conditional' GC, one quantifies the extent to which the past of Y assists in predicting the future of × beyond the extent to which the pasts of × and all other variables excluding Y predict the future of X. These approaches lead to distinct functional connectivity maps. The conditional approach potentially describes better how each system component processes and communicates distinct information; however it is practically more awkward because it requires estimating more parameters. We review the elegant mathematical properties of the linear autoregressive approach to GC that make it an attractive connectivity measure, e.g. its links to information theory and its insensitivity to amplification level [2,3]. We then discuss challenges in the application to electrophysiology data, such as non-stationarity and statistical bias. We present ways to overcome these challenges, involving data segmentation and permutation analysis [4], and modifications to the basic linear autoregression model. We validate our methods in two ways: first using simulated data from models for which true GC values can be analytically derived; second using simulated local field potential data from a large-scale spiking neuron simulator. The latter allows us to confirm, for the first time, that GC analysis can indeed correctly reproduce the underlying causal structure in realistic neural dynamics. We compare and contrast the ability of pairwise and conditional GC approaches to detect significant changes in functional connectivity between wake, sleep and anaesthesia states across recorded scalp and intracranial EEG variables. Analyses are performed in both the time and frequency domains, and we make use of the first ever code for computing conditional GC in the frequency domain. As predicted, pairwise GC is more sensitive to changes in brain state than full conditional GC; however conditional GC performed on just a few simultaneous variables at once can also give meaningful results. In summary, we demonstrate rigorous methodology and new code for GC analysis of steady-state data, and illustrate the utility of GC in exposing the functional neural interactions underlying different brain states.

Prolongation of the Q-T interval in man during sleep

Somatic reflex response-reversal of reticular origin