scholarly journals A Brief Introduction to Causal Inference

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Son Phuc Nguyen ◽  
Keyword(s):  
Artificial Intelligence ◽  
Causal Inference ◽  
Nobel Prize ◽  
Economic Policies ◽  
High Stake ◽  
Cause And Effect

Causal inference has been of interest in economics for many decades with a great deal of notable work like the Granger’s causality which directly lead to a Nobel Prize in Economics. The question of cause and effect is of paramount importance in making high-stake decisions such as economic policies. Besides, in the last ten years, causal inference in artificial intelligence has gradually become a mainstream with remarkable work such as the do-calculus by Judea Pearl. In this paper, we would like to discuss some fundamental ideas in causal inference.

scholarly journals Blockchain Economical Models, Delegated Proof of Economic Value and Delegated Adaptive Byzantine Fault Tolerance and their implementation in Artificial Intelligence BlockCloud

2019 ◽  
Vol 12 (4) ◽  
pp. 177 ◽  
Author(s):  
Qi Deng
Keyword(s):  
Artificial Intelligence ◽  
Fault Tolerance ◽  
Large Scale ◽  
Economic Value ◽  
Economic Models ◽  
Consensus Algorithm ◽  
Economic Policies ◽  
Byzantine Fault Tolerance ◽  
Distributed Consensus ◽  
Byzantine Fault

The Artificial Intelligence BlockCloud (AIBC) is an artificial intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer (the latter three are the collective “upper-layers”). The AIBC layers have distinguished responsibilities and thus performance and robustness requirements. The upper layers need to follow a set of economic policies strictly and run on a deterministic and robust protocol. While the fundamental layer needs to follow a protocol with high throughput without sacrificing robustness. As such, the AIBC implements a two-consensus scheme to enforce economic policies and achieve performance and robustness: Delegated Proof of Economic Value (DPoEV) incentive consensus on the upper layers, and Delegated Adaptive Byzantine Fault Tolerance (DABFT) distributed consensus on the fundamental layer. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to enforce the DPoEV, as well as to achieve the best balance of performance, robustness, and security. The DPoEV-DABFT dual-consensus architecture, by design, makes the AIBC attack-proof against risks such as double-spending, short-range and 51% attacks; it has a built-in dynamic sharding feature that allows scalability and eliminates the single-shard takeover. Our contribution is four-fold: that we develop a set of innovative economic models governing the monetary, trading and supply-demand policies in the AIBC; that we establish an upper-layer DPoEV incentive consensus algorithm that implements the economic policies; that we provide a fundamental layer DABFT distributed consensus algorithm that executes the DPoEV with adaptability; and that we prove the economic models can be effectively enforced by AIBC’s DPoEV-DABFT dual-consensus architecture.

scholarly journals Behavioral Implications of Causal Misperceptions

2020 ◽  
Vol 12 (1) ◽  
pp. 81-106
Author(s):  
Ran Spiegler
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Bayesian Networks ◽  
Causal Inference ◽  
Graphical Models ◽  
Computational Analysis ◽  
Decision Makers ◽  
Equilibrium Models ◽  
Economic Applications ◽  
Basic Concepts

This review presents an approach to modeling decision making under misspecified subjective models. The approach is based on the idea that decision makers impose subjective causal interpretations on observed correlations, and it borrows basic concepts and tools from the statistics and artificial intelligence literatures on Bayesian networks. While these background literatures used Bayesian networks as a platform for normative and computational analysis of probabilistic and causal inference, in the framework proposed here graphical models represent causal misperceptions and help analyze their behavioral implications. I show how this approach sheds light on earlier equilibrium models with nonrational expectations and demonstrate its scope of economic applications.

A Simplified Logic of Causal Inference

1987 ◽  
Vol 21 (4) ◽  
pp. 507-513 ◽  
Author(s):  
Wayne Hall
Keyword(s):  
Causal Inference ◽  
Causal Claim ◽  
Causal Explanations ◽  
Cause And Effect ◽  
Research Designs ◽  
Simplified Method ◽  
The Relationship

This paper provides a simplified method for evaluating the evidence in favour of a causal claim. It analyses the evidence bearing upon such a claim in terms of two questions: Do the putative cause and effect covary, and can alternative non-causal explanations of the relationship be ruled out? The different research designs for assessing covariation are outlined, as are the ways in which these designs permit a researcher to decide between alternative explanations of the relationship.

scholarly journals Artificial Intelligence, Globalization, and Strategies for Economic Development

2021 ◽  
pp. 1-53
Author(s):  
Anton Korinek ◽  
Joseph Stiglitz
Keyword(s):  
Artificial Intelligence ◽  
Developing Countries ◽  
New Technologies ◽  
Developed Countries ◽  
Economic Policies ◽  
Resource Saving ◽  
Technological Advances ◽  
Poverty And Inequality ◽  
Past Half Century ◽  
Winner Takes All

Progress in artificial intelligence and related forms of automation technologies threatens to reverse the gains that developing countries and emerging markets have experienced from integrating into the world economy over the past half century, aggravating poverty and inequality. The new technologies have the tendency to be labor-saving, resource-saving, and to give rise to winner-takes-all dynamics that advantage developed countries. We analyze the economic forces behind these developments and describe economic policies that would mitigate the adverse effects on developing and emerging economies while leveraging the potential gains from technological advances. We also describe reforms to our global system of economic governance that would share the benefits of AI more widely with developing countries.

scholarly journals What I Wish I Had Known Early in Graduate School but Didn’t

AI Magazine ◽  
2020 ◽  
Vol 41 (1) ◽  
pp. 90-100
Author(s):  
Sven Koenig
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Nobel Prize ◽  
Graduate School ◽  
Southern California ◽  
University Of Southern California ◽  
The University ◽  
Turing Award

Begin with the end in mind!1 PhD students in artificial intelligence can start to prepare for their career after their PhD degree immediately when joining graduate school, and probably in many more ways than they think. To help them with that, I asked current PhD students and recent PhD computer-science graduates from the University of Southern California and my own PhD students to recount the important lessons they learned (perhaps too late) and added the advice of Nobel Prize and Turing Award winners and many other researchers (including my own reflections), to create this article.

Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence

2011 ◽  
pp. 290-313
Author(s):  
Christian Hillbrand
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Forecasting Model ◽  
Empirical Validation ◽  
Functional Dependencies ◽  
Cause And Effect ◽  
Decision Supporting ◽  
Approximate Simulation

The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect of certain business variables. However, the soundness of these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional dependencies underlying these associations turns out to be the biggest issue for the quality of the results of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic management as well as management science. However, one can find proven causality techniques in other sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents an approach which applies a combination of well-established statistical causal proofing methods to strategy models in order to validate them. These validated causal strategy models are then used as the basis for approximating the functional form of causal dependencies by the means of Artificial Neural Networks. This in turn can be employed to build an approximate simulation or forecasting model of the strategic system.

General AI

2020 ◽  
pp. 194-206
Author(s):  
Stephen K. Reed
Keyword(s):  
Artificial Intelligence ◽  
Standard Model ◽  
Nobel Prize ◽  
Cognitive Skills ◽  
General Purpose ◽  
Intelligence Tests ◽  
Grand Challenges ◽  
Wide Range ◽  
The Mind

People use their cognitive skills to solve a wide range of problems whereas computers solve only a limited number of specific problems. A goal of artificial intelligence (AI) is to build on its previous success in specific environments to advance toward the generality of human level intelligence. People are efficient general-purpose learners who can adapt to many situations such as navigating in spatial environments and communicating by using language. To compare human and machine reasoning the AI community has proposed a standard model of the mind. Measuring progress in achieving general AI will require a wide variety of intelligence tests. Grand challenges, such as helping scientists win a Nobel prize, should stimulate development efforts.

scholarly journals RUSSIAN-TO-ENGLISH SIMULTANEOUS HUMAN TRANSLATION OF CAUSE-AND-EFFECT RELATIONS VS. GOOGLE TRANSLATE

2021 ◽  
Author(s):  
Natalya V. Nikulina ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Translation ◽  
English Translation ◽  
Semantic Features ◽  
Cause And Effect ◽  
Internet Resources ◽  
Research Material ◽  
Russian Linguistic ◽  
Simultaneous Translation ◽  
Linguistic Means

The paper emphasizes that the study of Google Translate capacities in simultaneous translation might be relevant due to the advances in machine translation based on artificial intelligence technologies. The research material includes transcripts of public speeches and their Russian-to-English translation collected from the Official Internet Resources of the President of Russia [http://kremlin.ru/] as well as Russian-to-English translation of the speeches via Google Translate. The paper analyses structural and semantic features of Russian linguistic means that convey cause-and-effect relations and reveals the ways of simultaneous human and machine interpreting them into English.

KALKINMIŞLIK FARKI VE NEDEN-SONUÇ İLİŞKİSİ BAĞLAMINDA KIRSALDAN KENTE YAPILAN GÖÇLER: TÜRKİYE’DE TRA2 BÖLGESİ ÜZERİNE AMPİRİK BİR ANALİZ MIGRATIONS FROM RURAL TO CITY IN CONTEXT DIFFERENCE OF DEVELOPMENT AND CAUSE-AND-EFFECT RELATIONSHIP: AN EMPIRICA

2013 ◽  
Vol 1 (1) ◽  
pp. 16
Author(s):  
Adem Karakaş ◽  
Cavit Yeşilyurt ◽  
Yüksel Koçak
Keyword(s):  
Health Services ◽  
Public Services ◽  
Economic Conditions ◽  
Primary Data ◽  
Economic Policies ◽  
Social Disorder ◽  
Effect Relationship ◽  
Welfare Level ◽  
Cause And Effect ◽  
Made In

Development distinction between regions and countries is the most essential triggering element of migration. Migration arising from wills related to living in better economic conditions, getting better education, getting better health services and generally living at higher socioeconomic welfare level bring out several consequences for both places left and recently settled. Social disorder and economic instability occur when public services halt, welfare level starts to decrease in populated areas and therewith social and economic levels decrease in areas left. These problems should be solved at base for carrying out practices directed to stop migration. General social and economic policies necessary for stopping migration and remigration should be determined and in this framework national and international implementation should be made. In this study, causes and effects on immigrants of migration have been analyzed and evaluations about possible practices for remigration have been made by using primary data. 

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