Dynamic Hierarchical Model Research Articles

A New Location-Allocation Model for Blood Distribution Considering Limited Lifespan Under Fuzzy Conditions

One of the important centers among the health facilities is the centers of blood donations. Blood donors may not be able to donate because of the long distance between blood donation centers and their location. In this article, a dynamic hierarchical location-allocation model with fuzzy conditions is offered to locate blood donation centers and assign blood donors to these centers. The limited life span for blood is considered as the most important model assumption. Because of this in real world situations, fuzzy theory and uncertainty approaches will be applied to formulate the problem. In addition, the total amount of donated blood for each area is not definite. The ratio between different expiration dates and blood depends on blood donations during different periods, so this parameter is faced with uncertainty. Numerical examples are presented to show benefits the and the performance of proposed model. In addition, the proposed model is run for a real-world case in the city of Tehran, Iran. The results indicate that applying the proposed optimization model can improve amount of shortage and inventory in the blood network against current status in the case study. Besides, experiments indicate that applying fuzzy theory for this problem can reduce 12.5% of total costs via the certain formulation.

Intentions: The dynamic hierarchical model revisited.

Ten years ago, one of us proposed a dynamic hierarchical model of intentions that brought together philosophical work on intentions and empirical work on motor representations and motor control (Pacherie, 2008). The model distinguished among Distal intentions, Proximal intentions, and Motor intentions operating at different levels of action control (hence the name DPM model). This model specified the representational and functional profiles of each type of intention, as well their local and global dynamics, and the ways in which they interact. A core insight of the model was that action control is the result of integrated, coordinated activity across these levels of intention. Since the proposal of the model, empirical and theoretical works in philosophy and cognitive science have emerged that would seem to support and expand on this central insight. In particular, an updated understanding of the nature of sensorimotor processing and motor representations, as well as of how the different levels of intention and control interface and interact, allows for the further specification and precisification of the original DPM model. This article is categorized under: Philosophy > Psychological Capacities Psychology > Motor Skill and Performance Philosophy > Action.

The role of regional and sectoral factors in Russian inflation developments

This paper examines the relative roles of region-specific and commodity-specific developments in the consumer price setting in Russia. For this purpose, we estimate a dynamic hierarchical factor model using inflation rates across regions and sectors. We found little evidence of association between region-specific factors and inflation developments, although there are several regions (mostly located in the Far East and North Caucasus) where the idiosyncratic component may contribute substantially. Conversely, the role of cross-commodity relative price changes in inflation developments in Russia is substantial.

Marketing Budget Allocation Across Countries: The Role of International Business Cycles

A key conundrum facing organizations is how to adjust marketing budgets in response to the business cycle. While most firms use procyclical spending (spending less during economic contractions), academic studies often recommend countercyclical spending (spending more during contractions), which begs the following question: What is the right thing to do? The spending problem is compounded further when demand is not just driven by one country’s business cycle, but by the (nonsynchronized) business cycles of multiple countries, as is the case for tourism marketing aiming to attract tourists originating from different countries. We derive insights into the best way to allocate marketing budgets across countries under varying economic conditions. We show that the allocation decisions are driven by the procyclical versus countercyclical nature of three factors: unit sales, marketing effectiveness, and per-unit profit contribution. To study how unit sales and marketing effectiveness respond to the business cycle, we develop a transfer function dynamic hierarchical linear model. We also model the responsiveness of the profit contribution to the business cycle. In an application to New Zealand tourism marketing, we find that a reallocation of the government’s marketing budget could yield an increase in tourist revenues of NZD $121 million. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1046 .

Monetary Policy, Hot Money and Housing Price Growth Across Chinese Cities

We use a dynamic hierarchical factor model to identify the national, regional, and local factors of the city-level housing price growth in China from 2005 to 2015. During the zero-lower-bound (ZLB) episode in the U.S., local factors account for 78% of variations in the month-on-month city-level housing price growth. However, as the time horizon gets longer and longer, the national factor gets a larger and larger variance share. When the horizon is extended to half a year, the variance share of the national factor reaches 51%. This indicates that the city-level housing price growth in China is more of a national wide phenomenon in recent years. We then use a VAR model to investigate the driving forces of the national factor and find that monetary policy and hot money shocks can affect the national housing price growth significantly. A positive monetary policy shock has a significant negative impact on the national factor, which lasts for more than two years. A positive hot money shock does cause a significant increase in the national factor. However, this effect is transitory and gets reversed in half a year. Our analysis indicates that the reversed effect of hot money shocks and the negative impact of positive price shocks on the national factor result from the tightening of monetary policy triggered by these shocks.

Reliable and efficient hierarchical organization model for computational grid

Although the hierarchical model appears to be an effective solution to organize the resource managements in grid systems which have more stringent demand for both scalability and efficiency, it has some limitations which need to be addressed. For example, the master/manager resources in different levels represent single points of failure and they may be sources of bottleneck and communication overhead especially if they are not efficiently selected. Moreover, the dynamic and fault-prone nature of grids cannot be treated by static structures while the manual construction and repairing are also prohibitive due to the highly caused overhead which often represents a significant obstruction to an efficient resource utilization (especially for those with intermittent availabilities). The main objective of this paper is to first introduce a self-repairing n-try dynamic hierarchical grid model for scheduling and load balancing in which each master resource will be replicated on one of its children resources. Second, an efficient methodology to elect masters–replicas resources is proposed. In this methodology, the masters–replicas are selected based on both resource reliability (in terms of MTBF) and resource proximity from the other nodes in specified groups (in terms of communication latency). Validation of the proposed methodology based on the proposed model is done via simulation. Experimental results show that the proposed model has a great impact on the overall performance. Compared to other approaches, the simulations show that our approach decrements the average completion time (ACT) by 18.9%–25%, increases the tree stability ratio (TSR) up to 26.2%–27.1%, and minimizes the total communication overhead (TCO) by 4.4%–18.7% in the range of system parameter values examined.

Dynamic hierarchical models for monetary transmission

Monetary policies, either actual or perceived, cause changes in monetary interest rates. These changes impact the economy through financial institutions, which react to changes in the monetary policy with changes in their administered rates, on both deposits and lendings. In this paper we provide a dynamic modeling for describing how administered bank interest rates react in response to changes in money market rates, in a multicountry setting: in addition, by means of hierarchical equations, we take into account how such changes are affected by the macroeconomic fundamentals of each country. The paper applies the proposed models to interest rates on different loans (to corporates and families) in seven European economies, showing how the monetary policy and the specific situation of each country have differently impacted lendings across time.

Modeling the Costs of Trade Finance During the Financial Crisis of 2008-2009: An Application of Dynamic Hierarchical Linear Model

The authors propose a dynamic hierarchical linear model (DHLM) to study the variations in the costs of trade finance across multiple countries during the global financial crisis of 2008-2009. Specifically, they examine how the impact of a set of four macroeconomic indicators on trade finance costs varied in and around the financial crisis. They find that countries with higher GDP growth faced lower costs of trade finance and countries with higher trade intensity (Trade/GDP) experienced higher trade finance costs in 2009 and 2010. Somewhat surprisingly, the countries with more stock market capitalization compared to GDP also faced higher costs of trade finance during and post crisis. Finally, inflation had a weak statistically significant impact on trade finance costs in 2009. The authors propose extensions to the model and discuss its alternative uses in different contexts.

The role of country-specific fundamentals in sovereign CDS spreads: Eastern European experiences

We investigate the determinants of sovereign CDS spreads on a sample of Eastern European data. A dynamic hierarchical factor model is used to aggregate information in indicators of economic fundamentals. CDS spreads are regressed on forecasts of factors. We find that domestic fundamentals explain more of CDS spread variance than global factors, largely due to their ability to explain differences in sovereign risk across countries. The effects on CDS spreads are found to be time-varying. In terms of economic significance, the factor of institutional-political strength stands out. We apply the model to study CDS spreads of Poland, Russia and Turkey.

Population–reaction model and microbial experimental ecosystems for understanding hierarchical dynamics of ecosystems

Understanding ecosystem dynamics is crucial as contemporary human societies face ecosystem degradation. One of the challenges that needs to be recognized is the complex hierarchical dynamics. Conventional dynamic models in ecology often represent only the population level and have yet to include the dynamics of the sub-organism level, which makes an ecosystem a complex adaptive system that shows characteristic behaviors such as resilience and regime shifts. The neglect of the sub-organism level in the conventional dynamic models would be because integrating multiple hierarchical levels makes the models unnecessarily complex unless supporting experimental data are present. Now that large amounts of molecular and ecological data are increasingly accessible in microbial experimental ecosystems, it is worthwhile to tackle the questions of their complex hierarchical dynamics. Here, we propose an approach that combines microbial experimental ecosystems and a hierarchical dynamic model named population–reaction model. We present a simple microbial experimental ecosystem as an example and show how the system can be analyzed by a population–reaction model. We also show that population–reaction models can be applied to various ecological concepts, such as predator–prey interactions, climate change, evolution, and stability of diversity. Our approach will reveal a path to the general understanding of various ecosystems and organisms.

Effects of Prey Presence and Scale on Bobcat Resource Selection during Winter.

Factors relevant to resource selection in carnivores may vary across spatial and temporal scales, both in magnitude and rank. Understanding relationships among carnivore occupancy, prey presence, and habitat characteristics, as well as their interactions across multiple scales, is necessary to improve our understanding of resource selection and predict population changes. We used a multi-scale dynamic hierarchical co-occurrence model with camera data to study bobcat and snowshoe hare occupancy in the Upper Peninsula of Michigan during winter 2012–2013. Bobcat presence was influenced at the local scale by snowshoe hare presence, and by road density at the local and larger scale when hare were absent. Hare distribution was related primarily to vegetation cover types, and detectability varied in space and time. Bobcat occupancy dynamics were influenced by different factors depending on the spatial scale considered and the resource availability context. Moreover, considering observed co-occurrence, we suggest that bobcat presence had a greater effect on hare occupancy than hare presence on bobcat occupancy. Our results highlight the importance of studying carnivore distributions in the context of predator-prey relationships and its interactions with environmental covariates at multiple spatial scales. Our approach can be applied to other carnivore species to provide insights beneficial for management and conservation.

Mutual and asynchronous anticipation and action in sports as globally competitive and locally coordinative dynamics.

Humans interact by changing their actions, perceiving other’s actions and executing solutions in conflicting situations. Using oscillator models, nonlinear dynamics have been considered for describing these complex human movements as an emergence of self-organisation. However, these frameworks cannot explain the hierarchical structures of complex behaviours between conflicting inter-agent and adapting intra-agent systems, especially in sport competitions wherein mutually quick decision making and execution are required. Here we adopt a hybrid multiscale approach to model an attack-and-defend game during which both players predict the opponent’s movement and move with a delay. From both simulated and measured data, one synchronous outcome between two-agent (i.e. successful defence) can be described as one attractor. In contrast, the other coordination-breaking outcome (i.e. successful attack) cannot be explained using gradient dynamics because the asymmetric interaction cannot always assume a conserved physical quantity. Instead, we provide the asymmetric and asynchronous hierarchical dynamical models to discuss two-agent competition. Our framework suggests that possessing information about an opponent and oneself in local-coordinative and global-competitive scale enables us to gain a deeper understanding of sports competitions. We anticipate developments in the scientific fields of complex movement adapting to such uncontrolled environments.

Intrinsic defect formation in peptide self-assembly

In contrast to extensively studied defects in traditional materials, we report here a systematic investigation of the formation mechanism of intrinsic defects in self-assembled peptide nanostructures. The Monte Carlo simulations with our simplified dynamic hierarchical model revealed that the symmetry breaking of layer bending mode at the two ends during morphological transformation is responsible for intrinsic defect formation, whose microscopic origin is the mismatch between layer stacking along the side-chain direction and layer growth along the hydrogen bond direction. Moreover, defect formation does not affect the chirality of the self-assembled structure, which is determined by the initial steps of the peptide self-assembly process.

The dynamics of international capital flows: Results from a dynamic hierarchical factor model

The present paper examines the degree of comovement of gross capital inflows, which is a highly sensitive issue for policy makers. We estimate a dynamic hierarchical factor model that is able to decompose inflows in a sample of 47 economies into (i) a global factor common to all types of flows and all recipient countries, (ii) a factor specific to a given type of capital inflows, (iii) a regional factor and (iv) a country-specific component. We find that the latter explains by far the largest fraction of fluctuations in capital inflows followed by regional factors, which are particularly important for emerging markets' FDI and portfolio inflows as well as bank lending to Emerging Europe. The global factor, however, explains only a small share of the overall variation. We also employ dynamic panel and time series regressions to explain the determinants of the estimated components. The global factor is shown to reflect U.S. financial conditions.

The Evolution and Planning of Hierarchical Transport Networks

The continuing growth of urban regions coupled with their strong interactions with transportation systems places great emphasis on the coevolution of these systems. In this paper we introduce three developments. First, as a preliminary, the dynamic model of the evolution of retail structure is extended to show how hierarchies emerge. Secondly, these ideas are applied to a model of the evolution in transport networks. Thirdly, it is shown that such a model can be translated into a planning context and the notion of an explicitly hierarchical dynamic model is introduced. An example of the use of such a model of evolution within a network planning context is presented. This explores the planning of network evolution in a case study of Naples arising from a new harbour which is under construction. The model provides a new method for transport system planning through combining the insights from dynamic modelling, particularly with respect to the sequencing of investment, with the practicalities of budget constraints.

Reconsidering the International Comovement of Inflation

In this paper we reconsider the degree of international comovement of inflation rates. We use a dynamic hierarchical factor model that is able to decompose Consumer Price Index (CPI) inflation in a panel of countries into (i) a factor common to all inflation series and all countries, (ii) a factor specific to a given sub-section of the CPI, (iii) a country group-factor and (iv) a country-specific component. With its pyramidal structure, the model allows for the possibility that the global factor affects the country-group factor and other subordinated factors but not vice versa. Using quarterly data for industrialized and emerging economies from 1996 to 2011 we find that about two thirds of overall inflation volatility is due to country-specific determinants. For CPI inflation net of food and energy, the global factor and the CPI basket-specific factor account for less than 20 % of inflation variation. Only energy price inflation in industrial economies is dominated by common factors.

Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance

We study the relative importance of online word of mouth and advertising on firm performance over time since product introduction. The current research separates the volume of consumer-generated online word of mouth (OWOM) from its valence, which has three dimensions—attribute, emotion, and recommendation oriented. Firm-initiated advertising content is also classified as attribute or emotion advertising. We also shed light on the role played by advertising content on generating the different types of OWOM conversations. We use a dynamic hierarchical linear model (DHLM) for our analysis. The proposed model is compared with a dynamic linear model, vector autoregressive/system of equations model, and a generalized Bass model. Our estimation accounts for potential endogeneity in the key measures. Among the different OWOM measures, only the valence of recommendation OWOM is found to have a direct impact on sales; i.e., not all OWOM is the same. This impact increases over time. In contrast, the impact of attribute advertising and emotion advertising decreases over time. Also, consistent with prior research, we observe that rational messages (i.e., attribute-oriented advertising) wears out a bit faster than emotion-oriented advertising. Moreover, the volume of OWOM does not have a significant impact on sales. This suggests that, in our data, “what people say” is more important than “how much people say.” Next, we find that recommendation OWOM valence is driven primarily by the valence of attribute OWOM when the product is new and driven by the valence of emotion OWOM when the product is more mature. Our brand-level results help us classify brands as consumer driven or firm driven, depending on the relative importance of the OWOM and advertising measures, respectively.

Hierarchical dynamic models for multivariate times series of counts

In several application areas, we see the need for accurate statistical modeling of multivariate time series of counts as a function of relevant covariates. In ecology, count responses on species abundance are observed over several time periods at several locations, and the covariates that influence the abundance may be location-specific and/or time-varying. This paper describes a Bayesian framework for estimation and prediction by assuming a multivariate Poisson sampling distribution for the count responses and by fitting a hierarchical dynamic model. Our modeling incorporates the temporal dependence as well as dependence between the components of the response vector.

A Neurocomputational Model of the Mismatch Negativity

The mismatch negativity (MMN) is an event related potential evoked by violations of regularity. Here, we present a model of the underlying neuronal dynamics based upon the idea that auditory cortex continuously updates a generative model to predict its sensory inputs. The MMN is then modelled as the superposition of the electric fields evoked by neuronal activity reporting prediction errors. The process by which auditory cortex generates predictions and resolves prediction errors was simulated using generalised (Bayesian) filtering – a biologically plausible scheme for probabilistic inference on the hidden states of hierarchical dynamical models. The resulting scheme generates realistic MMN waveforms, explains the qualitative effects of deviant probability and magnitude on the MMN – in terms of latency and amplitude – and makes quantitative predictions about the interactions between deviant probability and magnitude. This work advances a formal understanding of the MMN and – more generally – illustrates the potential for developing computationally informed dynamic causal models of empirical electromagnetic responses.

Bayesian hierarchical age-period-cohort models with time-structured effects: An application to religious voting in the US, 1972–2008

To examine dynamics of political processes using repeated cross-section data, effects of age, cohort, and time period have to be disentangled. I propose a Bayesian dynamic hierarchical model with cohort and period effects modeled as random walk through time. It includes smoothly time-varying effects of covariates, allowing researchers to study changing effects of individual characteristics on political behavior. It provides a flexible functional form estimate of age by integrating a semi-parametric approach in the hierarchical model. I employ this approach to examine religious voting in the United States using repeated cross-sectional surveys from 1972 to 2008. I find starkly differing nonlinear trends of de- and re-alignment among different religious denominations.