Macro Trend: Anticipatory Behavioral Economics

We are good at imagining what we'd do with an extra million dollars; however, we fare poorly when life presents us with unexpected losses or outflows of cash. By creating a game for consumers to reallocate spending after an "extreme event," we hope to help them to react more rationally to the unexpected. Do people behave differently if the cause of loss is exogenous rather than due to poor planning?

Companies would like more new products to be successful in the marketplace, but current evaluation methods such as focus groups do not accurately predict customer decisions. We are developing new technology-assisted methods to try to improve the customer-evaluation process and better predict customer decisions. The new methods involve multi-modal affective measures (such as facial expression and skin conductance) together with behavioral measures, anticipatory-motivational measures, and self-report cognitive measures.

Recent findings in affective neuroscience and psychology indicate that human affect and emotional experience play a significant and useful role in human learning and decision-making. Most machine-learning and decision-making models, however, are based on old, purely cognitive models, and are slow, brittle, and awkward to adapt. We aim to redress many of these classic problems by developing new models that integrate affect with cognition. Ultimately, such improvements will allow machines to make smarter and more human-like decisions for better human-machine interaction.