pwc-archive/papers-with-abstracts huggingface dataset:
Cooperativity, information gain, and energy cost during early LTP in dendritic spines
Authors: Jan Karbowski, Paulina Urban
Date: 2024-04-22 00:00:00

We investigate a mutual relationship between information and energy during early phase of LTP induction and maintenance in a large-scale system of mutually coupled dendritic spines, with discrete internal states and probabilistic dynamics, within the framework of nonequilibrium stochastic thermodynamics. In order to analyze this computationally intractable stochastic multidimensional system, we introduce a pair approximation, which allows us to reduce the spine dynamics into a lower dimensional manageable system of closed equations. It is found that the rates of information gain and energy attain their maximal values during an initial period of LTP (i.e. during stimulation), and after that they recover to their baseline low values, as opposed to a memory trace that lasts much longer. This suggests that learning phase is much more energy demanding than the memory phase. We show that positive correlations between neighboring spines increase both a duration of memory trace and energy cost during LTP, but the memory time per invested energy increases dramatically for very strong positive synaptic cooperativity, suggesting a beneficial role of synaptic clustering on memory duration. In contrast, information gain after LTP is the largest for negative correlations, and energy efficiency of that information generally declines with increasing synaptic cooperativity. We also find that dendritic spines can use sparse representations for encoding of long-term information, as both energetic and structural efficiencies of retained information and its lifetime exhibit maxima for low fractions of stimulated synapses during LTP. In general, our stochastic thermodynamics approach provides a unifying framework for studying, from first principles, information encoding and its energy cost during learning and memory in stochastic systems of interacting synapses.
Transitions and Thermodynamics on Species Graphs of Chemical Reaction Networks
Authors: Keisuke Sugie, Dimitri Loutchko, Tetsuya J. Kobayashi
Date: 2024-04-22 00:00:00

Chemical reaction networks (CRNs) exhibit complex dynamics governed by their underlying network structure. In this paper, we propose a novel approach to study the dynamics of CRNs by representing them on species graphs (S-graphs). By scaling concentrations by conservation laws, we obtain a graph representation of transitions compatible with the S-graph, which allows us to treat the dynamics in CRNs as transitions between chemicals. We also define thermodynamic-like quantities on the S-graph from the introduced transitions and investigate their properties, including the relationship between specieswise forces, activities, and conventional thermodynamic quantities. Remarkably, we demonstrate that this formulation can be developed for a class of irreversible CRNs, while for reversible CRNs, it is related to conventional thermodynamic quantities associated with reactions. The behavior of these specieswise quantities is numerically validated using an oscillating system (Brusselator). Our work provides a novel methodology for studying dynamics on S-graphs, paving the way for a deeper understanding of the intricate interplay between the structure and dynamics of chemical reaction networks.
Functions of Direct and Indirect Pathways for Action Selection Are Quantitatively Analyzed in A Spiking Neural Network of The Basal Ganglia
Authors: Sang-Yoon Kim, Woochang Lim
Date: 2024-04-22 00:00:00

We are concerned about action selection in the basal ganglia (BG). We quantitatively analyze functions of direct pathway (DP) and indirect pathway (IP) for action selection in a spiking neural network with 3 competing channels. For such quantitative analysis, in each channel, we obtain the competition degree ${\cal C}_d$, given by the ratio of strength of DP (${\cal S}_{DP}$) to strength of IP (${\cal S}_{IP}$) (i.e., ${\cal C}_d = {\cal S}_{DP} / {\cal S}_{IP}$). Then, a desired action is selected in the channel with the largest ${\cal C}_d$. Desired action selection is made mainly due to strong focused inhibitory projection to the output nucleus, SNr (substantia nigra pars reticulata) via the DP in the corresponding channel. Unlike the case of DP, there are two types of IPs; intra-channel IP and inter-channel IP, due to widespread diffusive excitation from the STN (subthalamic nucleus). The intra-channel IP serves a function of brake to suppress the desired action selection. In contrast, the inter-channel IP to the SNr in the neighboring channels suppresses competing actions, leading to highlight the desired action selection. In this way, function of the inter-channel IP is opposite to that of the intra-channel IP. However, to the best of our knowledge, no quantitative analysis for such functions of the DP and the two IPs was made. Here, through direct calculations of the DP and the intra- and the inter-channel IP presynaptic currents into the SNr in each channel, we obtain the competition degree of each channel to determine a desired action, and then functions of the DP and the intra- and inter-channel IPs are quantitatively made clear.
Stochastic Volatility in Mean: Efficient Analysis by a Generalized Mixture Sampler
Authors: Daichi Hiraki, Siddhartha Chib, Yasuhiro Omori
Date: 2024-04-22 00:00:00

In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2007), we develop an accurate approximation of the non-central chi-squared distribution as a mixture of thirty normal distributions. Under this mixture representation, we sample the parameters and latent volatilities in one block. We also detail a correction of the small approximation error by using additional Metropolis-Hastings steps. The proposed method is extended to the SVM model with leverage. The methodology and models are applied to excess holding yields and S&P500 returns in empirical studies, and the SVM models are shown to outperform other volatility models based on marginal likelihoods.
Identification and Estimation of Nonseparable Triangular Equations with Mismeasured Instruments
Authors: Shaomin Wu
Date: 2024-04-21 00:00:00

In this paper, I study the nonparametric identification and estimation of the marginal effect of an endogenous variable $X$ on the outcome variable $Y$, given a potentially mismeasured instrument variable $W^*$, without assuming linearity or separability of the functions governing the relationship between observables and unobservables. To address the challenges arising from the co-existence of measurement error and nonseparability, I first employ the deconvolution technique from the measurement error literature to identify the joint distribution of $Y, X, W^*$ using two error-laden measurements of $W^*$. I then recover the structural derivative of the function of interest and the "Local Average Response" (LAR) from the joint distribution via the "unobserved instrument" approach in Matzkin (2016). I also propose nonparametric estimators for these parameters and derive their uniform rates of convergence. Monte Carlo exercises show evidence that the estimators I propose have good finite sample performance.
How do applied researchers use the Causal Forest? A methodological review of a method
Authors: Patrick Rehill
Date: 2024-04-20 00:00:00

This methodological review examines the use of the causal forest method by applied researchers across 133 peer-reviewed papers. It shows that the emerging best practice relies heavily on the approach and tools created by the original authors of the causal forest such as their grf package and the approaches given by them in examples. Generally researchers use the causal forest on a relatively low-dimensional dataset relying on observed controls or in some cases experiments to identify effects. There are several common ways to then communicate results -- by mapping out the univariate distribution of individual-level treatment effect estimates, displaying variable importance results for the forest and graphing the distribution of treatment effects across covariates that are important either for theoretical reasons or because they have high variable importance. Some deviations from this common practice are interesting and deserve further development and use. Others are unnecessary or even harmful. The paper concludes by reflecting on the emerging best practice for causal forest use and paths for future research.
On the Asymmetric Volatility Connectedness
Authors: Abdulnasser Hatemi-J
Date: 2024-04-19 00:00:00
Tasks: Time Series

Connectedness measures the degree at which a time-series variable spills over volatility to other variables compared to the rate that it is receiving. The idea is based on the percentage of variance decomposition from one variable to the others, which is estimated by making use of a VAR model. Diebold and Yilmaz (2012, 2014) suggested estimating this simple and useful measure of percentage risk spillover impact. Their method is symmetric by nature, however. The current paper offers an alternative asymmetric approach for measuring the volatility spillover direction, which is based on estimating the asymmetric variance decompositions introduced by Hatemi-J (2011, 2014). This approach accounts explicitly for the asymmetric property in the estimations, which accords better with reality. An application is provided to capture the potential asymmetric volatility spillover impacts between the three largest financial markets in the world.
The modified conditional sum-of-squares estimator for fractionally integrated models
Authors: Mustafa R. Kılınç, Michael Massmann
Date: 2024-04-19 00:00:00

In this paper, we analyse the influence of estimating a constant term on the bias of the conditional sum-of-squares (CSS) estimator in a stationary or non-stationary type-II ARFIMA ($p_1$,$d$,$p_2$) model. We derive expressions for the estimator's bias and show that the leading term can be easily removed by a simple modification of the CSS objective function. We call this new estimator the modified conditional sum-of-squares (MCSS) estimator. We show theoretically and by means of Monte Carlo simulations that its performance relative to that of the CSS estimator is markedly improved even for small sample sizes. Finally, we revisit three classical short datasets that have in the past been described by ARFIMA($p_1$,$d$,$p_2$) models with constant term, namely the post-second World War real GNP data, the extended Nelson-Plosser data, and the Nile data.
Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
Authors: Shaomin Wu
Date: 2024-04-19 00:00:00

In this paper, I characterize the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through individual fixed effects. Using data from a single large network, I propose a two-step estimator for the model primitives. In the first step, I estimate agents' equilibrium beliefs of other people's choice probabilities. In the second step, I plug in the first-step estimator to the conditional choice probability expression and estimate the model parameters and the unobserved individual fixed effects together using Joint MLE. Assuming that the observed attributes are discrete, I showed that the first step estimator is uniformly consistent with rate $N^{-1/4}$, where $N$ is the total number of linking proposals. I also show that the second-step estimator converges asymptotically to a normal distribution at the same rate.
Axiomatic modeling of fixed proportion technologies
Authors: Xun Zhou, Timo Kuosmanen
Date: 2024-04-18 00:00:00
Tasks: Misconceptions

Understanding input substitution and output transformation possibilities is critical for efficient resource allocation and firm strategy. There are important examples of fixed proportion technologies where certain inputs are non-substitutable and/or certain outputs are non-transformable. However, there is widespread confusion about the appropriate modeling of fixed proportion technologies in data envelopment analysis. We point out and rectify several misconceptions in the existing literature, and show how fixed proportion technologies can be correctly incorporated into the axiomatic framework. A Monte Carlo study is performed to demonstrate the proposed solution.
A Global Minimum Tax for Large Firms Only: Implications for Tax Competition
Authors: Andreas Haufler, Hayato Kato
Date: 2024-04-22 00:00:00

The Global Minimum Tax (GMT) is applied only to firms above a certain size threshold, permitting countries to set differential tax rates for small and large firms. We analyze tax competition among multiple tax havens and a non-haven country for heterogeneous multinationals to evaluate the effects of this partial coverage of GMT. Upon the introduction of a moderately low GMT rate, the havens commit to the single uniform GMT rate for all multinationals. However, gradual increases in the GMT rate induce the havens, and subsequently the non-haven, to adopt discriminatory, lower tax rates for small multinationals. Our calibration exercise shows that the implementation of a 15% GMT rate results in a regime where only the havens adopt split tax rates. Upon GMT introduction, welfare and tax revenues fall in the tax havens but rise in the non-haven, yielding a positive net gain worldwide.
Natural Capital as a Stock Option
Authors: O. Bertolami
Date: 2024-04-22 00:00:00

The unfolding climate crisis is a physical manifestation of the damage that market economy, driven by the high intensity consumption of fossil fuels, has inflicted on the Earth System and on the stability conditions that were established by a complex conjugation of natural factors during the Holoecene. The magnitude of the human activities and its predatory nature is such that it is no longer possible to consider the Earth System and the services it provides for the habitability of the planet, the so-called natural capital, as an economical externality. Thus one is left with two main choices in what concerns the sustainability of the planet's habitability: radical economic degrowth or highly efficient solutions to internalise the maintenance and the restoration of ecosystems and the services of the Earth System. It is proposed that an interesting strategy for the latter is to consider the natural capital as a stock option.
A separate way to measure rate of return
Authors: Gordon Getty, Nikita Tkachenko
Date: 2024-04-22 00:00:00

Net profit is sometimes found from data for net operating surplus. We propose a way to find it from data for consumption, pay and market-value capital, and concomitantly to reveal the factor shares in consumption.
The Economics of Blockchain Governance: Evaluate Liquid Democracy on the Internet Computer
Authors: Yulin Liu, Luyao Zhang
Date: 2024-04-21 00:00:00

Decentralized Autonomous Organizations (DAOs), utilizing blockchain technology to enable collective governance, are a promising innovation. This research addresses the ongoing query in blockchain governance: How can DAOs optimize human cooperation? Focusing on the Network Nervous System (NNS), a comprehensive on-chain governance framework underpinned by the Internet Computer Protocol (ICP) and liquid democracy principles, we employ theoretical abstraction and simulations to evaluate its potential impact on cooperation and economic growth within DAOs. Our findings emphasize the significance of the NNS's staking mechanism, particularly the reward multiplier, in aligning individual short-term interests with the DAO's long-term prosperity. This study contributes to the understanding and effective design of blockchain-based governance systems.
Long-term forecasts of statewide travel demand patterns using large-scale mobile phone GPS data: A case study of Indiana
Authors: Rajat Verma, Eunhan Ka, Satish V. Ukkusuri
Date: 2024-04-19 00:00:00

The growth in availability of large-scale GPS mobility data from mobile devices has the potential to aid traditional travel demand models (TDMs) such as the four-step planning model, but those processing methods are not commonly used in practice. In this study, we show the application of trip generation and trip distribution modeling using GPS data from smartphones in the state of Indiana. This involves extracting trip segments from the data and inferring the phone users' home locations, adjusting for data representativeness, and using a data-driven travel time-based cost function for the trip distribution model. The trip generation and interchange patterns in the state are modeled for 2025, 2035, and 2045. Employment sectors like industry and retail are observed to influence trip making behavior more than other sectors. The travel growth is predicted to be mostly concentrated in the suburban regions, with a small decline in the urban cores. Further, although the majority of the growth in trip flows over the years is expected to come from the corridors between the major urban centers of the state, relative interzonal trip flow growth will likely be uniformly spread throughout the state. We also validate our results with the forecasts of two travel demand models, finding a difference of 5-15% in overall trip counts. Our GPS data-based demand model will contribute towards augmenting the conventional statewide travel demand model developed by the state and regional planning agencies.
Use of two Public Distributed Ledgers to track the money of an economy
Authors: Gonzalo Garcia-Atance Fatjo
Date: 2024-04-19 00:00:00

A tool to improve the effectiveness and the efficiency of public spending is proposed here. In the 19th century banknotes had a serial number. However, in modern days the use of digital transactions that do not use physical currency has opened the possibility to digitally track almost each cent of the economy. In this article a serial number or tracking number for each cent, pence or any other monetary unit of the economy is proposed. Then, almost all cents can be tracked by recording the transactions in a public distributed ledger, rather than recording the amount of the transaction, the information recorded in the block of the transaction is the actual serial number or tracking number for each cent that changes ownership. In order to keep the privacy of the transaction, only generic identification of private companies and individuals are recorded along with generic information about the concept of transaction, the region and the date/time. A secondary public distributed ledger whose blocks are identified by a hash reference that is recorded in the bank statement available to the payer and the payee allows for checking the accuracy of the first public distributed ledger by comparing the transactions made in one day, one region and one type of concept. However, the transactions made or received by the government are recorded with a much higher level of detail in the first ledger and a higher level of disclosure in the second ledger. The result is a tool that is able to accurately track public spending, to keep privacy of individuals and companies and to make statistical analysis and experiments or real tests in the economy of a country. This tool has the potential to assist public policymakers in demonstrating the societal benefits resulting from their policies, thereby enabling more informed decision-making for future policy endeavours.
How Gender and Birth Order Affect Educational attainment Inequality within-Families: Evidence from Benin
Authors: Christelle Zozoungbo
Date: 2024-04-19 00:00:00

This paper examines how gender, birth order, and innate ability shape within-household disparities in children's educational attainment in developing countries. Using data from Benin, I find that in households with non-educated parents, gender and birth order drive over two-thirds of the average educational attainment disparities among adult children, while their influence decreases to one-third in households with college-educated parents. Furthermore, average inequality, measured by the range of children's educational attainment is twice as high among non-educated parents compared to college-educated parents. I propose and estimate a structural model of educational attainment choices within-family. Using the model, I show that the absence of gender and birth order effects does not lead to a significant reduction in the average within-family disparities in children's educational attainment. Additionally, in theory, ensuring that every child has at least one year of education lowers average within-family educational inequality. Yet, even in this scenario, daughters tend to receive less education than sons, and practical efforts to achieve universal entry are less effective than the theoretical model.
Sustainable regional economic development and land use: a case of Russia
Authors: Wadim Strielkowski, Oxana Mukhoryanova, Oxana Kuznetsova, Yury Syrov
Date: 2024-04-18 00:00:00
Tasks: Management

This paper analyzes sustainable regional economic development and land use employing a case study of Russia. The economics of land management in Russia which is shaped by both historical legacies and contemporary policies represents an interesting conundrum. Following the dissolution of the Soviet Union, Russia embarked on a thorny and complex path towards the economic reforms and transformation characterized, among all, by the privatization and decentralization of land ownership. This transition was aimed at improving agricultural productivity and fostering sustainable regional economic development but also led to new challenges such as uneven distribution of land resources, unclear property rights, and underinvestment in rural infrastructure. However, managing all of that effectively poses significant challenges and opportunities. With the help of the comprehensive bibliographic network analysis, this study sheds some light on the current state of sustainable regional economic development and land use management in Russia. Its results and outcomes might be helpful for the researchers and stakeholders alike in devising effective strategies aimed at maximizing resources for sustainable land use, particularly within their respective regional economies.
On fusing active and passive acoustic sensing for simultaneous localization and mapping
Authors: Aidan J. Bradley, Nicole Abaid
Date: 2024-04-19 00:00:00
Tasks: Simultaneous Localization and Mapping

Studies on the social behaviors of bats show that they have the ability to eavesdrop on the signals emitted by conspecifics in their vicinity. They can fuse this ``passive" data with actively collected data from their own signals to get more information about their environment, allowing them to fly and hunt more efficiently and to avoid or cause jamming when competing for prey. Acoustic sensors are capable of similar feats but are generally used in only an active or passive capacity at one time. Is there a benefit to using both active and passive sensing simultaneously in the same array? In this work we define a family of models for active, passive, and fused sensing systems to measure range and bearing data from an environment defined by point-based landmarks. These measurements are used to solve the problem of simultaneous localization and mapping (SLAM) with extended Kalman filter (EKF) and FastSLAM 2.0 approaches. Our results show agreement with previous findings. Specifically, when active sensing is limited to a narrow angular range, fused sensing can perform just as accurately if not better, while also allowing the sensor to perceive more of the surrounding environment.