Performing interventions is a major challenge in economic policy-making. We propose causal strategic inference as a framework for conducting interventions and apply it to large, networked microfinance economies. The basic solution platform consists of modeling a microfinance market as a networked economy, learning the parameters of the model from the real-world microfinance data, and designing algorithms for various causal questions. For a special case of our model, we show that an equilibrium point always exists and that the equilibrium interest rates are unique. For the general case, we give a constructive proof of the existence of an equilibrium point. Our empirical study is based on the microfinance data from Bangladesh and Bolivia, which we use to first learn our models. We show that causal strategic inference can assist policy-makers by evaluating the outcomes of various types of interventions, such as removing a loss-making bank from the market, imposing an interest rate cap, and subsidizing banks.

Performing interventions is a major challenge in economic policy-making. We propose causal strategic inference as a framework for conducting interventions and apply it to large, networked microfinance economies. The basic solution platform consists of modeling a microfinance market as a networked economy, learning the parameters of the model from the real-world microfinance data, and designing algorithms for various causal questions. For a special case of our model, we show that an equilibrium point always exists and that the equilibrium interest rates are unique. For the general case, we give a constructive proof of the existence of an equilibrium point. Our empirical study is based on the microfinance data from Bangladesh and Bolivia, which we use to first learn our models. We show that causal strategic inference can assist policy-makers by evaluating the outcomes of various types of interventions, such as removing a loss-making bank from the market, imposing an interest rate cap, and subsidizing banks.

Computer modeling of physical systems must accommodate uncertainty in materials and loading conditions. This input uncertainty translates into a stochastic response from the model, based on nominal settings of a physical system, even when the simulator is deterministic. In engineering, assessing the reliability of said system can mean guarding against a physical collapse, puncture or failing of electronics. Reliability statistics like failure probability, the probability the response exceeds a threshold, can be calculated with Monte Carlo (MC). While MC produces an asymptotically unbiased estimator (Robert and Casella 2013), it can take thousands or even millions of model evaluations, i.e., great computational expense, to achieve a desired error tolerance. The search for alternatives to direct MC in computer-assisted reliability analysis has become a cottage industry of late. Some approaches seek to gradually reduce the design space for sampling through subset selection (Cannamela et al. 2008; Au and Beck 2001). Importance sampling (IS) focuses MC efforts by biasing sampling toward areas of the design space where failure is probable (Srinivasan 2013), and then re-weights any expectations to correct for that bias asymptotically. Effective IS strategies (Li et al. 2011; Peherstorfer et al. 2018a) aim to generate samples which reduce variance compared to direct MC.

Banks in Singapore take the lead on artificial intelligence (AI) training. Open banking: what you need to know By Christoffer O. Hernæs, chief digital officer, S-Banken Open banking will fundamentally change banking the same way internet banking once did. As banks become integrated parts of digital ecosystems, the distribution of banking products will change and in the end become more valuable in the right context for the end customer. How artificial intelligence (AI), embedded tech and experience design are reframing banking. The world is changing around big financial services organisations.

Performing interventions is a major challenge in economic policy-making. We propose \emph{causal strategic inference} as a framework for conducting interventions and apply it to large, networked microfinance economies. The basic solution platform consists of modeling a microfinance market as a networked economy, learning the parameters of the model from the real-world microfinance data, and designing algorithms for various computational problems in question. We adopt Nash equilibrium as the solution concept for our model. For a special case of our model, we show that an equilibrium point always exists and that the equilibrium interest rates are unique. For the general case, we give a constructive proof of the existence of an equilibrium point. Our empirical study is based on the microfinance data from Bangladesh and Bolivia, which we use to first learn our models. We show that causal strategic inference can assist policy-makers by evaluating the outcomes of various types of interventions, such as removing a loss-making bank from the market, imposing an interest rate cap, and subsidizing banks.