Numeric Data Frames and Probabilistic Judgments in Complex Real-World Environments

This thesis investigates human probabilistic judgment in complex real-world settings to identify processes underpinning biases across groups which relate to numerical frames and formats. Experiments are conducted replicating real-world environments and data to test judgment performance based on framing and format. Regardless of background skills and experience, people in professional and consumer contexts show a strong tendency to perceive the world from a linear perspective, interpreting information in concrete, absolute terms and making judgments based on seeking and applying linear functions. Whether predicting sales, selecting between financial products, or forecasting refugee camp data, people use minimal cues and systematically apply additive methods amidst non-linear trends and percentage points to yield linear estimates in both rich and sparse informational contexts. Depending on data variability and temporality, human rationality and choice may be significantly helped or hindered by informational framing and format. The findings deliver both theoretical and practical contributions. Across groups and individual differences, the effects of informational format and the tendency to linearly extrapolate are connected by the bias to perceive values in concrete terms and make sense of data by seeking simple referent points. People compare and combine referents using additive methods when inappropriate and adhere strongly to defaults when applied in complex numeric environments. The practical contribution involves a framing manipulation which shows that format biases (i.e., additive processing) and optimism (i.e., associated with intertemporal effects) can be counteracted in judgments involving percentages and exponential growth rates by using absolute formats and positioning defaults in future event context information. This framing manipulation was highly effective in improving loan choice and repayment judgments compared to information in standard finance industry formats. There is a strong potential to increase rationality using this data format manipulation in other financial settings and domains such as health behaviour change in which peoples’ erroneous interpretation of percentages and non-linear relations negatively impact choice and behaviours in both the short and long-term

Accelerated Genetic Programming of Polynomials

An accelerated polynomial construction technique for genetic programming is proposed. This is a horizontal technique for gradual expansion of a partial polynomial during traversal of its tree-structured representation. The coefficients of the partial polynomial and the coefficient of the new term are calculated by a rapid recurrent least squares (RLS) fitting method. When used for genetic programming (GP) of polynomials this technique enables us not only to achieve fast estimation of the coefficients, but also leads to power series models that differ from those of traditional Koza-style GP and from those of the previous GP with polynomials STROGANOFF. We demonstrate that the accelerated GP is sucessful in that it evolves solutions with greater generalization capacity than STROGANOFF and traditional GP on symbolic regression, pattern recognition, and financial time-series prediction tasks