The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational expectation, game theory, efficient market hypotheses, mechanism design, prospect, bounded rationality, portfolio theory, rational counterfactual and causality. The benefit of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.

In [1] we presented a model for transactions when goods are given away in the expectation of a later settlement. In settings where people keep track of their social accounts we were able to redefine concepts like account balance, yield curve and the law of diminishing returns. In this paper we establish a general equilibrium theorem, conjectured in [1], by developing sufficient conditions for any instance of the standard model (or Gift Economy Model) to have a unique equilibrium. The convergence to that equilibrium is exponential and for each pair of entities P and Q the total sum of yields from all mutual transactions is equal to zero. [1] W.P. Weijland, Mathematical Foundations for the Economy of Giving, ArXiv Categories: q-fin.GN, Report 1401.4664, 2014.

Most people are risk-averse (risk-seeking) when they expect to gain (lose). Based on a generalization of ``expected utility theory'' which takes this into account, we introduce an automaton mimicking the dynamics of economic operations. Each operator is characterized by a parameter q which gauges people's attitude under risky choices; this index q is in fact the entropic one which plays a central role in nonextensive statistical mechanics. Different long term patterns of average asset redistribution are observed according to the distribution of parameter q (chosen once for ever for each operator) and the rules (e.g., the probabilities involved in the gamble and the indebtedness restrictions) governing the values that are exchanged in the transactions. Analytical and numerical results are discussed in terms of how the sensitivity to risk affects the dynamics of economic transactions.

If a cat, a cannonball, and an economics textbook are all dropped from the same height, they fall to the ground with exactly the same acceleration under the influence of gravity. This equality of gravitational accelerations of different things is one of the most accurately tested laws of physics. That law, however, tells us little about cats, cannonballs, or economics. This lecture expands on this theme to address the question of what features of our world are predicted by a fundamental ``theory of everything'' governing the regularities exhibited universally by all physical systems. This may consist of two parts: a dynamical law governing regularities in time (e.g superstring theory) and a law of cosmological initial condition governing mostly regularities in space (e.g. Hawking's no-boundary initial condition). The lecture concludes that: (1) ``A theory of everything'' is not a theory of everything in a quantum mechanical universe. (2) If the laws are short enough to be discoverable then they are probably too short to predict everything. (3) The regularities of human history, economics, biology, geology, etc are consistent with the fundamental laws of physics but do not follow from them. (Public lecture given at The Future of Theoretical Physics and Cosmology: Stephen Hawking 60th Birthday Symposium.)

This chapter provides a concise survey on different dissipativity conditions that have appeared in the literature on economic model predictive control and discusses their decisive role in this context.

This paper examines the time series properties of cryptocurrency assets, such as Bitcoin, using established econometric inference techniques, namely models of the GARCH family. The contribution of this study is twofold. I explore the time series properties of cryptocurrencies, a new type of financial asset on which there appears to be little or no literature. I suggest an improved econometric specification to that which has been recently proposed in Chu et al (2017), the first econometric study to examine the price dynamics of the most popular cryptocurrencies. Questions regarding the reliability of their study stem from the authors mis-diagnosing the distribution of GARCH innovations. Checks are performed on whether innovations are Gaussian or GED by using Kolmogorov type non-parametric tests and Khmaladze's martingale transformation. Null of gaussianity is strongly rejected for all GARCH(p,q) models, with $p,q \in \{1,\ldots,5 \}$, for all cryptocurrencies in sample. For tests of normality, I make use of the Gauss-Kronrod quadrature. Parameters of GARCH models are estimated with generalized error distribution innovations using maximum likelihood. For calculating P-values, the parametric bootstrap method is used. Arguing against Chu et al (2017), I show that there is a strong empirical argument against modelling innovations under some common assumptions.

Let $\mathbb{Q}$ and $\mathbb{P}$ be equivalent probability measures and let $ψ$ be a $J$-dimensional vector of random variables such that $\frac{d\mathbb{Q}}{d\mathbb{P}}$ and $ψ$ are defined in terms of a weak solution $X$ to a $d$-dimensional stochastic differential equation. Motivated by the problem of \emph{endogenous completeness} in financial economics we present conditions which guarantee that every local martingale under $\mathbb{Q}$ is a stochastic integral with respect to the $J$-dimensional martingale $S_t \set \mathbb{E}^{\mathbb{Q}}[ψ|\mathcal{F}_t]$. While the drift $b=b(t,x)$ and the volatility $σ= σ(t,x)$ coefficients for $X$ need to have only minimal regularity properties with respect to $x$, they are assumed to be analytic functions with respect to $t$. We provide a counter-example showing that this $t$-analyticity assumption for $σ$ cannot be removed.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

The number, importance, and popularity of rankings measuring innovation performance and the strength and resources of ecosystems that provide its spatial framework are on an increasing trend globally. In addition to influencing the specific decisions taken by economic actors, these rankings significantly impact the development of innovation-related policies at regional, national, and international levels. The importance of startup ecosystems is proven by the growing scientific interest, which is demonstrated by the increasing number of related scientific articles. The concept of the startup ecosystem is a relatively new category, the application of which in everyday and scientific life has been gaining ground since the end of the 2000s. In parallel, of course, the demand for measurability and comparability has emerged among decision-makers and scholars. This demand is met by startup ecosystem rankings, which now measure and rank the performance of individual ecosystems on a continental and global scale. However, while the number of scientific publications examining rankings related to higher education, economic performance, or even innovation, can be measured in the order of thousands, scientific research has so far rarely or tangentially addressed the rankings of startup ecosystems. This study and the related research intend to fill this gap by presenting and analysing the characteristics of global rankings and identifying possible future research directions.

Assessments of impacts of climate change and future projections over the Indian region, have so far relied on a single regional climate model (RCM) - eg., the PRECIS RCM of the Hadley Centre, UK. While these assessments have provided inputs to various reports (e.g., INCCA 2010; NATCOMM2 2012), it is important to have an ensemble of climate projections drawn from multiple RCMs due to large uncertainties in regional-scale climate projections. Ensembles of multi-RCM projections driven under different perceivable socio-economic scenarios are required to capture the probable path of growth, and provide the behavior of future climate and impacts on various biophysical systems and economic sectors dependent on such systems. The Centre for Climate Change Research, Indian Institute of Tropical Meteorology (CCCR-IITM) has generated an ensemble of high resolution downscaled projections of regional climate and monsoon over South Asia until 2100 for the Intergovernmental Panel for Climate Change (IPCC)using a RCM (ICTP-RegCM4) at 50 km horizontal resolution, by driving the regional model with lateral and lower boundary conditions from multiple global atmosphere-ocean coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The future projections are based on three Representation Concentration Pathway (RCP) scenarios (viz., RCP2.6, RCP4.5, RCP8.5) of the IPCC.