E-commerce provides an efficient and effective way to exchange goods between sellers and customers. E-commerce has been a popular method for doing business, because of its simplicity of having commerce activity transparently available, including customer voice and opinion about their own experience. Those experiences can be a great benefit to understand customer experience comprehensively, both for sellers and future customers. This paper applies to e-commerces and customers in Indonesia. Many Indonesian customers expressed their voice to open social network services such as Twitter and Facebook, where a large proportion of data is in the form of conversational data. By understanding customer behavior through open social network service, we can have descriptions about the e-commerce services level in Indonesia. Thus, it is related to the government's effort to improve the Indonesian digital economy ecosystem. A method for finding core topics in large-scale internet unstructured text data is needed, where the method should be fast but sufficiently accurate. Processing large-scale data is not a straightforward job, it often needs special skills of people and complex software and hardware computer system. We propose a fast methodology of text mining methods based on frequently appeared words and their word association to form network text methodology. This method is adapted from Social Network Analysis by the model relationships between words instead of actors.

Bounded rationality is an important consideration stemming from the fact that agents often have limits on their processing abilities, making the assumption of perfect rationality inapplicable to many real tasks. We propose an information-theoretic approach to the inference of agent decisions under Smithian competition. The model explicitly captures the boundedness of agents (limited in their information-processing capacity) as the cost of information acquisition for expanding their prior beliefs. The expansion is measured as the Kullblack-Leibler divergence between posterior decisions and prior beliefs. When information acquisition is free, the \textit{homo economicus} agent is recovered, while in cases when information acquisition becomes costly, agents instead revert to their prior beliefs. The maximum entropy principle is used to infer least-biased decisions, based upon the notion of Smithian competition formalised within the Quantal Response Statistical Equilibrium framework. The incorporation of prior beliefs into such a framework allowed us to systematically explore the effects of prior beliefs on decision-making, in the presence of market feedback. We verified the proposed model using Australian housing market data, showing how the incorporation of prior knowledge alters the resulting agent decisions. Specifically, it allowed for the separation (and analysis) of past beliefs and utility maximisation behaviour of the agent.

Campbell-Goodhart's law relates to the causal inference error whereby decision-making agents aim to influence variables which are correlated to their goal objective but do not reliably cause it. This is a well known error in Economics and Political Science but not widely labelled in Artificial Intelligence research. Through a simple example, we show how off-the-shelf deep Reinforcement Learning (RL) algorithms are not necessarily immune to this cognitive error. The off-policy learning method is tricked, whilst the on-policy method is not. The practical implication is that naive application of RL to complex real life problems can result in the same types of policy errors that humans make. Great care should be taken around understanding the causal model that underpins a solution derived from Reinforcement Learning.

This chapter aims to critically review the existing literature on the relationship between corporate social responsibility (CSR) and corporate governance features. Drawn on management and corporate governance theories, we develop a theoretical model that makes explicit the links between board diversity, CSR committees' attributes, CSR and financial performance. Particularly, we show that focusing on the cognitive and demographic characteristics of board members could provide more insights on the link between corporate governance and CSR. We also highlight how the functioning and the composition of CSR committees, could be valuable to better understand the relationship between corporate governance and CSR.

This paper uses new and recently introduced methodologies to study the similarity in the dynamics and behaviours of cryptocurrencies and equities surrounding the COVID-19 pandemic. We study two collections; 45 cryptocurrencies and 72 equities, both independently and in conjunction. First, we examine the evolution of cryptocurrency and equity market dynamics, with a particular focus on their change during the COVID-19 pandemic. We demonstrate markedly more similar dynamics during times of crisis. Next, we apply recently introduced methods to contrast trajectories, erratic behaviours, and extreme values among the two multivariate time series. Finally, we introduce a new framework for determining the persistence of market anomalies over time. Surprisingly, we find that although cryptocurrencies exhibit stronger collective dynamics and correlation in all market conditions, equities behave more similarly in their trajectories, extremes, and show greater persistence in anomalies over time.

Many problems in quantitative finance involve both predictive forecasting and decision-based optimization. Traditionally, predictive models are optimized with unique prediction-based objectives and constraints, and are therefore unaware of how those predictions will ultimately be used in the context of their final decision-based optimization. We present a stochastic optimization framework for integrating regression based predictive models in a mean-variance portfolio optimization setting. Closed-form analytical solutions are provided for the unconstrained and equality constrained case. For the general inequality constrained case, we make use of recent advances in neural-network architecture for efficient optimization of batch quadratic-programs. To our knowledge, this is the first rigorous study of integrating prediction in a mean-variance portfolio optimization setting. We present several historical simulations using global futures data and demonstrate the benefits of the integrated approach in comparison to the decoupled alternative.

We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.

Work has now begun on the sixth generation of cellular technologies (`6G`) and cost-efficient global broadband coverage is already becoming a key pillar. Indeed, we are still far from providing universal and affordable broadband connectivity, despite this being a key part of the Sustainable Development Goals (Target 9.c). Currently, both Mobile Network Operators and governments still lack independent analysis of the strategies that can help achieve this target with the cellular technologies available (4G and 5G). Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies. As an example, the assessment approach is applied to India, the world`s second-largest mobile market and a country with notoriously high spectrum prices. The results demonstrate the trade-offs between technological decisions. This includes demonstrating how important current infrastructure policy is, particularly given fiber backhaul will be essential for delivering 6G quality of service. We find that by eliminating the spectrum licensing costs, full 5G population coverage can viably be achieved using fiber backhaul. In conclusion, supportive 5G infrastructure policies are essential in providing a superior foundation for evolution to 6G.

Rawls' theory of justice aims at fairness. He does not only think of justice between exiting parties in existing society, but he also thinks of it between generations intergenerational justice problem. Rawls' solution to this problem is the saving principle, and he says that we are responsible for being just with the next generations. Wolf thinks of our responsibility for future generations as a kind of financial debt that we have to pay. He also develops the meaning of "saving" and says that it is not restricted to the monetary one. Wolf extends the definition of "saving" such that it includes investment on behalf of the next generations as well. In this paper, I want to extend the meaning of "saving" to "using the resources for sustainable and resilient systems." By referring to the problem of time, I show that our decision on behalf of the next generations will be rational only if we entirely use the natural resources and wealth to produce sustainable and resilient systems.

The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model flexibility when controlling for wage determinants, and iii) the choice of different parametric and semi-parametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39\% when we enforce comparability between men and women and use a more flexible specification of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50\% smaller and also less sensitive to the way wage determinants are included.

The internet's rapid growth stimulates the emergence of start-up companies based on information technology and telecommunication (ICT) in Indonesia and Singapore. As the number of start-ups and its investor growth, the network of its relationship become larger and complex, but on the other side feel small. Everyone in the ICT start-up investment network can be reached in short steps, led to a phenomenon called small-world phenomenon, a principle that we are all connected by a short chain of relationships. We investigate the pattern of the relationship between a start-up with its investor and the small world characteristics using network analysis methodology. The research is conducted by creating the ICT start-up investment network model of each country and calculate its small-world network properties to see the characteristic of the networks. Then we compare and analyze the result of each network model. The result of this research is to give knowledge about the current condition of ICT start-up investment in Indonesia and Singapore. The research is beneficial for business intelligence purposes to support decision-making related to ICT start-up investment.

In an infinitely repeated pricing game, pricing algorithms based on artificial intelligence (Q-learning) may consistently learn to charge supra-competitive prices even without communication. Although concerns on algorithmic collusion have arisen, little is known on underlying factors. In this work, we experimentally analyze the dynamics of algorithms with three variants of experience replay. Algorithmic collusion still has roots in human preferences. Randomizing experience yields prices close to the static Bertrand equilibrium and higher prices are easily restored by favoring the latest experience. Moreover, relative performance concerns also stabilize the collusion. Finally, we investigate the scenarios with heterogeneous agents and test robustness on various factors.