Voting Paradoxes and the Human Intuition

Brams (2003) presents three paradoxes for power indices: some rather counter-intuitive behaviour that is exhibited by both the Shapley-Shubik and the Banzhaf indices. We show that the proportional index is free from such paradoxical behaviour. This result suggests that our intuition may be based on the proportional index and as such its use in evaluating power measures is limited.Economics (Jel: A)

Iamblichus on Divination: Divine Power and Human Intuition

Across the ancient Graeco-Roman world, divination is among the most salient ways in which the power of the divine involves itself in the human world. Of course, one could wait for a miracle, but the gods were talking to us all the time, and it would have been an utterly common occurrence for ancient observers to sense their gods\u27 power emanating through the signs that were understood to course through the world around us. For decades, scholars have positioned these signs primarily as levers of social power. This has made the topic the province of historians and anthropologists seeking to gain a purchase on how those in control, and sometimes those from outside, harness the authority of the divine voice for their own ends. This approach has opened up rich veins of inquiry, with conversations across disciplinary boundaries and between students of different cultures and time periods

Evaluating Macroeconomic Forecasts: A Review of Some Recent Developments

Macroeconomic forecasts are frequently produced, published, discussed and used. The formal evaluation of such forecasts has a long research history. Recently, a new angle to the evaluation of forecasts has been addressed, and in this review we analyse some recent developments from that perspective. The literature on forecast evaluation predominantly assumes that macroeconomic forecasts are generated from econometric models. In practice, however, most macroeconomic forecasts, such as those from the IMF, World Bank, OECD, Federal Reserve Board, Federal Open Market Committee (FOMC) and the ECB, are based on econometric model forecasts as well as on human intuition. This seemingly inevitable combination renders most of these forecasts biased and, as such, their evaluation becomes non-standard. In this review, we consider the evaluation of two forecasts in which: (i) the two forecasts are generated from two distinct econometric models; (ii) one forecast is generated from an econometric model and the other is obtained as a combination of a model, the other forecast, and intuition; and (iii) the two forecasts are generated from two distinct combinations of different models and intuition. It is shown that alternative tools are needed to compare and evaluate the forecasts in each of these three situations. These alternative techniques are illustrated by comparing the forecasts from the Federal Reserve Board and the FOMC on inflation, unemployment and real GDP growth.Macroeconomic forecasts; econometric models; human intuition; biased forecasts; forecast performance; forecast evaluation; forecast comparison

Robust Counterfactual Explanations on Graph Neural Networks

Massive deployment of Graph Neural Networks (GNNs) in high-stake applications generates a strong demand for explanations that are robust to noise and align well with human intuition. Most existing methods generate explanations by identifying a subgraph of an input graph that has a strong correlation with the prediction. These explanations are not robust to noise because independently optimizing the correlation for a single input can easily overfit noise. Moreover, they do not align well with human intuition because removing an identified subgraph from an input graph does not necessarily change the prediction result. In this paper, we propose a novel method to generate robust counterfactual explanations on GNNs by explicitly modelling the common decision logic of GNNs on similar input graphs. Our explanations are naturally robust to noise because they are produced from the common decision boundaries of a GNN that govern the predictions of many similar input graphs. The explanations also align well with human intuition because removing the set of edges identified by an explanation from the input graph changes the prediction significantly. Exhaustive experiments on many public datasets demonstrate the superior performance of our method

A cooperative instinct

Acting on a gut feeling may sometimes lead to poor decisions, but it will usually support the common good, according to a study showing that human intuition favours cooperative, rather than selfish, behaviour