An information cascade is a circumstance where agents make decisions in a sequential fashion by following other agents. Bikhchandani et al., predict that once a cascade starts it continues, even if it is wrong, until agents receive an external input such as public information. In an information cascade, even if an agent has its own personal choice, it is always overridden by observation of previous agents' actions. This could mean agents end up in a situation where they may act without valuing their own information. As information cascades can have serious social consequences, it is important to have a good understanding of what causes them. We present a detailed Bayesian model of the information gained by agents when observing the choices of other agents and their own private information. Compared to prior work, we remove the high impact of the first observed agent's action by incorporating a prior probability distribution over the information of unobserved agents and investigate an alternative model of choice to that considered in prior work: weighted random choice. Our results show that, in contrast to Bikhchandani's results, cascades will not necessarily occur and adding prior agents' information will delay the effects of cascades.

It is widely known that India's job opportunities are not going at the same rate as its population. A recent report by National Sample Survey Office (NSSO) revealed that the unemployment rate was close to a four-decade high of 6.1% during 2017-18, compared to 2.2% in 2011-12. An added worry is the industry-wide application of artificial intelligence and machine learning. According to a study by McKinsey Global Institute, AI-based intelligent agents and robots may eliminate 30% of the workforce across the globe by 2030. However, according to Sanjeev Bikhchandani, cofounder of Info Edge (which owns platforms such as Naukri.com