Today's world is full of enormously inter-linked countries and businesses. Therefore, business planning decisions should take into account the linkages between different business segments as well as countries. Government policies also affect businesses not only in the home country but also elsewhere. However, typically business analysts who work on forecasts and planning do not account for changes in terms of policies and supply chains in their countries and elsewhere. In this session, we propose a framework that marries data-intensive computational methods employed in economic modeling with business decision making. The framework we propose is well-known in computational economics, called Computable General Equilibrium (CGE) model. It captures equilibrium in all markets in the economy, and tracks the movement from one equilibrium to another, in the presence of a perturbation from outside the system - like a policy shock, technological change or disaster. This framework employs publicly available rich information on linkages between countries and business segments to evaluate the market changes that can arise from major global and local policies as well as technological changes and disasters such as pandemics. This has immense potential to facilitate a scientific approach to handle future uncertainties involved in analytics for business decisions. This can be used for manufacturing sector or any other sector, to adjust their business strategies to external environments. Technically, we will discuss about data concepts in supply chain economics - called Input Output matrix-based models, which may further be built with behavioral equations that may be parametrized using AI/ML methods or conventional econometric techniques. In real time, we will show a classic example of this kind of a model - an open source global supply chain CGE model, named Global Trade Analysis Project (GTAP: www.gtap.org). We will conduct simulations to show how US-China trade war can affect different sectors in India, and how Covid-19 pandemic is impacting several sectors in India, based on our recent research with several international public organizations (ADB, UN, US Chamber of Commerce etc.) We shall provide study materials and links to download the software needed, ahead of the session. We will need atleast 45 minutes or 90 minutes (or even half a day session), to make sure that the audience can learn something actionable from this. These tools have been used by top consulting firms like McKinsey and almost all international organizations and governments, for example, for key decision making. There is also a lot of scope to use standard AI/ML tools herein. The purpose of this session is to both introduce these tools to the audience and also encourage them to collaborate on developing AI/ML solutions for some challenges in these tools, such as lack of accuracy in capturing the behavior of different components of the economy and forecasting the future.

Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.