Hasil untuk "Economic theory. Demography"

Sandro Ambuehl, Rahul Bhui, Heidi C. Thysen

A burgeoning literature in economics studies how people form beliefs about the causal structures linking economic variables, and what happens when those beliefs are mistaken. We survey this research and connect it to a rich literature in cognitive science. After providing an accessible introduction to causal Directed Acyclic Graphs, the dominant modeling approach, we review theory and evidence addressing three nested questions: how individuals reason within a fully parameterized causal structure, how they estimate its parameters, and how they learn such structures to begin with. We then discuss methodological challenges and review applications in microeconomics, macroeconomics, political economy, and business.

Daniel Roberts, Francesco Shankar, Vieri Cammelli et al.

Recent JWST observations have unveiled a numerous population of low-luminosity active galactic nuclei (AGN) at $4< z<10$, with space densities roughly an order of magnitude above pre-JWST estimates, and many of these AGN have masses orders of magnitude above the local black hole mass-stellar mass ($M_{\rm BH}-M_{*}$) scaling relations. We investigate the consistency of these observations within a data-driven framework that links the galaxy stellar mass function to the supermassive black hole (SMBH) mass function and AGN luminosity functions using different $M_{\rm BH}-M_{*}$ relations and the observed Eddington-ratio distribution. By comparing our predictions against observed AGN luminosity functions at $z\sim 5.5$ we find that observations can be reproduced either by highly-elevated $M_{\rm BH}-M_{*}$ relations paired with low duty cycles, or moderate relations with higher duty cycles. Through the Soltan argument, we find that $M_{\rm BH}-M_{*}$ relations that are modestly above the local relation for AGN produce consistency between multiple tracers of the SMBH demography at $z\sim 5.5$, while more extreme normalisations would require a weakly-evolving luminosity function at $z> 5.5$. Continuity-equation modelling shows that initially high $M_{\rm BH}-M_{*}$ relations predict a strong two-phase evolutionary scenario and very steep low-mass SMBH mass functions in tension with several current estimates, while more moderate relations generate local SMBH mass functions in better agreement with present determinations and near-constant scaling relations. Our results favour a scenario where SMBHs at $z \sim 5$ on average lie modestly above local AGN scaling relations, with elevated but physically plausible duty cycles. Future wide-field clustering and demographic studies will help break the remaining degeneracies between SMBH scaling relations and AGN duty cycles at early cosmic times.

Krishna Mani Nath, Bipan Hazarika, Hemanta Kalita

In this paper, we present a new fractal derivative with a nonsingular kernel and analyze its fundamental properties. The effectiveness of the proposed operator is illustrated through the study of economic models using both the Caputo fractal derivative and the new fractal derivative.

Navya Gupta, Christopher David White, Zohreh Davoudi

Quantum simulators hold great promise for studying real-time (Minkowski) dynamics of quantum field theories. Nonetheless, preparing non-trivial initial states remains a major obstacle. Euclidean-time Monte-Carlo methods yield ground-state spectra and static correlation functions that can, in principle, guide state preparation. In this work, we exploit this classical information to bridge Euclidean and Minkowski descriptions for a (1+1)-dimensional interacting scalar field theory. We propose variational ansatz families which achieve comparable ground-state energies, yet exhibit distinct correlations and local non-Gaussianity. By optimizing selected wavefunction moments with Monte-Carlo data, we obtain ansatzes that can be efficiently translated into quantum circuits. Our algorithmic cost analysis shows these circuits' gate complexity scales polynomially in system size. Our work paves the way for systematically leveraging classically-computed information to prepare initial states in quantum field theories of interest in nature.

Avik Ghosh, Adil Khurram, Jan Kleissl et al.

As opposed to stabilizing to a reference trajectory or state, Economic Model Predictive Control (EMPC) optimizes economic performance over a prediction horizon, making it particularly attractive for economic microgrid (MG) dispatch. However, as load and generation forecasts are only known 24-48 h in advance, economically optimal steady states or periodic trajectories are not available and the EMPC-based works that rely on these signals are inadequate. In addition, demand charges, based on maximum monthly grid import power of the MG, cannot be easily casted as an additive cost, which prevents the application of the principle of optimality if introduced naively. In this work, we propose to close this mismatch between the EMPC prediction horizon and existing monthly timescales by means of an appropriately generated baseline reference trajectory. To do this, we first propose an EMPC formulation for a generic deterministic discrete non-linear time-varying system subject to hard state and input constraints. We then show that, under mild assumptions on the terminal cost and region, the asymptotic average economic cost of the proposed method is no worse than a baseline given by any arbitrary reference trajectory that is only known online. In particular, this results into a practical, finite-time upper bound on the average economic cost difference with the baseline that decreases linearly to zero as time goes to infinity. We then show how the proposed EMPC framework can be used to solve optimal MG dispatch problems, introducing various costs and constraints that conform to the required assumptions. By means of this framework, we conduct realistic simulations with data from the Port of San Diego MG, which demonstrate that the proposed method can reduce monthly electricity costs in closed-loop with respect to established baseline reference trajectories.

Efe Gürel

In this paper, we expand the theory of Weierstrassian elliptic functions by introducing auxiliary zeta functions $ζ_λ$, zeta differences of first kind $Δ_λ$ and second kind $Δ_{λ,μ}$ where $λ,μ=1,2,3$. Fundamental and novel results pertaining to these functions are proven. Furthermore, results already existing in the literature are translated in terms of auxiliary zeta functions. Their relationship to Jacobian elliptic functions and Jacobian functions are given.

Mingxue Yan, Xuewen Zhang, Kaixiang Zhang et al.

In this paper, we propose a convex data-based economic predictive control method within the framework of data-enabled predictive control (DeePC). Specifically, we use a neural network to transform the system output into a new state space, where the nonlinear economic cost function of the underlying nonlinear system is approximated using a quadratic function expressed by the transformed output in the new state space. Both the neural network parameters and the coefficients of the quadratic function are learned from open-loop data of the system. Additionally, we reconstruct constrained output variables from the transformed output through learning an output reconstruction matrix; this way, the proposed economic DeePC can handle output constraints explicitly. The performance of the proposed method is evaluated via a case study in a simulated chemical process.

Ajit Desai

This article provides a curated review of selected papers published in prominent economics journals that use machine learning (ML) tools for research and policy analysis. The review focuses on three key questions: (1) when ML is used in economics, (2) what ML models are commonly preferred, and (3) how they are used for economic applications. The review highlights that ML is particularly used to process nontraditional and unstructured data, capture strong nonlinearity, and improve prediction accuracy. Deep learning models are suitable for nontraditional data, whereas ensemble learning models are preferred for traditional datasets. While traditional econometric models may suffice for analyzing low-complexity data, the increasing complexity of economic data due to rapid digitalization and the growing literature suggests that ML is becoming an essential addition to the econometrician's toolbox.

Mirhan Ürkmez, Carsten Kallesøe, Jan Dimon Bendtsen et al.

This paper deals with the control of pumps in large-scale water distribution networks with the aim of minimizing economic costs while satisfying operational constraints. Finding a control algorithm in combination with a model that can be applied in real-time is a challenging problem due to the nonlinearities presented by the pipes and the network sizes. We propose a predictive control algorithm with a periodic horizon. The method provides a way for the economic operation of large water networks with a small linear model. Economic Predictive control with a periodic horizon and a terminal state constraint is constructed to keep the state trajectories close to an optimal periodic trajectory. Barrier terms are also included in the cost function to prevent constraint violations. The proposed method is tested on the EPANET implementation of the water network of a medium size Danish town (Randers) and shown to perform as intended under varying conditions.

Jieao Zhu, Zhongzhichao Wan, Linglong Dai et al.

Traditional massive multiple-input multiple-output (MIMO) information theory adopt non-physically consistent assumptions, including white-noised, scalar-quantity, far-field, discretized, and monochromatic EM fields, which mismatch the nature of the underlying electromagnetic (EM) fields supporting the physical layer of wireless communication systems. To incorporate EM laws into designing procedures of the physical layer, we first propose the novel concept of EM physical layer, whose backbone theory is called EM information theory (EIT). In this article, we systematically investigate the basic ideas and main results of EIT. First, we review the fundamental analytical tools of classical information theory and EM theory. Then, we introduce the modeling and analysis methodologies of EIT, including continuous field modeling, degrees of freedom, and mutual information analyses. Several EIT-inspired applications are discussed to illustrate how EIT guides the design of practical wireless systems. Finally, we point out the open problems of EIT, where further research efforts are required for EIT to construct a unified interdisciplinary theory.

Samuel Braunfeld, Michael C. Laskowski

Given a complete theory $T$ and a subset $Y \subseteq X^k$, we precisely determine the {\em worst case complexity}, with respect to further monadic expansions, of an expansion $(M,Y)$ by $Y$ of a model $M$ of $T$ with universe $X$. In particular, although by definition monadically stable/NIP theories are robust under arbitrary monadic expansions, we show that monadically NFCP (equivalently, mutually algebraic) theories are the largest class that is robust under anything beyond monadic expansions. We also exhibit a paradigmatic structure for the failure of each of monadic NFCP/stable/NIP and prove each of these paradigms definably embeds into a monadic expansion of a sufficiently saturated model of any theory without the corresponding property.

Christian Saemann

I summarize and discuss some recent results on formulating actions of six-dimensional superconformal field theories using the language of higher gauge theory. The latter guarantees mathematical consistency of our constructions and we review crucial aspects of this framework, such as $L_\infty$-algebras and corresponding kinematical data given by higher connections. We then show that there is a mathematically consistent non-Abelian extension of the self-dual string equation which satisfies many physical expectations. Our construction favors a particular higher gauge group leading us to higher principal bundles known as string structures. Using these, we manage to formulate a six-dimensional action which shares many properties with the famous $(2,0)$-theory but also still differs from it in some key points.

Vanessa Haverd, Benjamin Smith, Lars Nieradzik et al.

CABLE is a land surface model (LSM) that can be applied stand-alone, as well as providing for land surface-atmosphere exchange within the Australian Community Climate and Earth System Simulator (ACCESS). We describe critical new developments that extend the applicability of CABLE for regional and global carbon-climate simulations, accounting for vegetation response to biophysical and anthropogenic forcings. A land-use and land-cover change module, driven by gross land-use transitions and wood harvest area was implemented, tailored to the needs of the Coupled Model Intercomparison Project-6 (CMIP6). Novel aspects include the treatment of secondary woody vegetation, which benefits from a tight coupling between the land-use module and the Population Orders Physiology (POP) module for woody demography and disturbance-mediated landscape heterogeneity. Land-use transitions and harvest associated with secondary forest tiles modify the annually-resolved patch age distribution within secondary-vegetated tiles, in turn affecting biomass accumulation and turnover rates and hence the magnitude of the secondary forest sink. Additionally, we implemented a novel approach to constrain modelled GPP consistent with the Co-ordination Hypothesis, predicted by evolutionary theory, which suggests that electron transport and Rubisco-limited rates adjust seasonally and across biomes to be co-limiting. We show that the default prior assumption - common to CABLE and other LSMs - of a fixed ratio of electron transport to carboxylation capacity is at odds with this hypothesis, and implement an alternative algorithm for dynamic optimisation of this ratio, such that co-ordination is achieved as an outcome of fitness maximisation. Results have significant implications the magnitude of the simulated CO2 fertilisation effect on photosynthesis in comparison to alternative estimates and observational proxies.

Huanchen Bao

We reformulate the Kazhdan-Lusztig theory for the BGG category $\mathcal{O}$ of Lie algebras of type D via the theory of canonical bases arising from quantum symmetric pairs initiated by Weiqiang Wang and the author. This is further applied to formulate and establish for the first time the Kazhdan-Lusztig theory for the BGG category $\mathcal{O}$ of the ortho-symplectic Lie superalgebra $\mathfrak{osp}(2m|2n)$.

Eamon Duede, Victor Zhorin

Macroeconomic theories of growth and wealth distribution have an outsized influence on national and international social and economic policies. Yet, due to a relative lack of reliable, system wide data, many such theories remain, at best, unvalidated and, at worst, misleading. In this paper, we introduce a novel economic observatory and framework enabling high resolution comparisons and assessments of the distributional impact of economic development through the remote sensing of planet earth's surface. Striking visual and empirical validation is observed for a broad, global macroeconomic sigma-convergence in the period immediately following the end of the Cold War. What is more, we observe strong empirical evidence that the mechanisms driving sigma-convergence failed immediately after the financial crisis and the start of the Great Recession. Nevertheless, analysis of both cross-country and cross-state samples indicates that, globally, disproportionately high growth levels and excessively high decay levels have become rarer over time. We also see that urban areas, especially concentrated within short distances of major capital cities were more likely than rural or suburban areas to see relatively high growth in the aftermath of the financial crisis. Observed changes in growth polarity can be attributed plausibly to post-crisis government intervention and subsidy policies introduced around the world. Overall, the data and techniques we present here make economic evidence for the rise of China, the decline of U.S. manufacturing, the euro crisis, the Arab Spring, and various, recent, Middle East conflicts visually evident for the first time.

Yongyang Cai, Kenneth L. Judd, Thomas S. Lontzek

There is great uncertainty about future climate conditions and the appropriate policies for managing interactions between the climate and the economy. We develop a multidimensional computational model to examine how uncertainties and risks in the economic and climate systems affect the social cost of carbon (SCC)---that is, the present value of the marginal damage to economic output caused by carbon emissions. The SCC is substantially increased by economic and climate risks at both current and future times. Furthermore, the SCC is itself a stochastic process with significant variation; for example, the basic elements of risk incorporated into our model cause the SCC in 2100 to be, with significant probability, ten times what it would be without those risks. We have only imprecise information about what parameter values are best for approximating reality. To deal with this parametric uncertainty we perform extensive uncertainty quantification and show that these findings are robust for a wide range of alternative specifications. More generally, this work shows that large-scale computing can enable economists to examine substantially more complex and realistic models for the purposes of policy analysis.

Tirthabir Biswas, Spyridon Talaganis

In String Theory there often appears a rather interesting class of higher derivative theories containing an infinite set of derivatives in the form of an exponential. These theories may provide a way to tame ultraviolet divergences without introducing ghost-like states. In this invited article we provide a brief overview on the progress that has been made over the last decade to construct such infinite derivative theories of gravity which may be able to address the singularity problems in gravity. In the process we will also be able to present some general results that applies to covariant torsion-free metric theories of gravity.

Davide Forcella, Alberto Zaffaroni

We consider three dimensional conformal field theories living on a stack of N anti-M2 branes at the tip of eight-dimensional supersymmetric cones. The corresponding supergravity solution is obtained by changing sign to the four-form in the Freund-Rubin solution representing M2 branes ("skew-whiffing" transformation) and it is known to be stable. The existence of these non supersymmetric, stable field theories, at least in the large N limit, is a peculiarity of the AdS4/CFT3 correspondence with respect to the usual AdS5/CFT4, and it is worthwhile to study it. We analyze in detail the KK spectrum of the skew-whiffed solution associated with S^7/Z_k and we speculate on the natural field content for a candidate non-supersymmetric dual field theory.

Martin Lüscher

A recently proposed formulation of chiral lattice gauge theories is reviewed, in which the locality and gauge invariance of the theory can be preserved if the fermion representation of the gauge group is anomaly-free.

H. Awata, S. Hirano, Y. Hyakutake

We study a membrane -- anti-membrane system in Matrix theory. It in fact exhibits the tachyon instability. By suitably representing this configuration, we obtain a (2+1)-dimensional U(2) gauge theory with a 't Hooft's twisted boundary condition. We identify the tachyon field with a certain off-diagonal element of the gauge fields in this model. Taking into account the boundary conditions carefully, we can find vortex solutions which saturate the Bogomol'nyi-type bound and manifest the tachyon condensation. We show that they can be interpreted as gravitons in Matrix theory.