Making Actuaries Less Human

Making Actuaries Less Human

Work by psychologists suggests
that, as human beings, actuaries are subject to a variety of mental biases and decision
making errors. The field of behavioural finance looks at how such biases affect
financial decisions.

This paper first looks at a selection of these biases including how:

• decisions are often made by adjusting from an existing position
  (anchoring)
  
• people are risk averse when facing gains but become risk seeking when
  facing losses (prospect theory)
  
• the framing of a problem can materially impact the decision that is made
  
• the frequency with which something is monitored can impact the decision
  (myopic loss aversion)
  
• people have a tendency to ignore underlying probability distributions
  
• almost everybody is overconfident
  
• when a number of different options are presented, the number, order and
  degree of difference between the options will affect which option is
  chosen.
  
• the use of separate mental accounts impacts financial decisions (mental
  accounting)
  

Reasoning the Fast and Frugal Way: Models of Bounded Rationality

Reasoning the Fast and Frugal Way: Models of Bounded Rationality

Humans and animals make inferences about the world under limited time and knowledge. In contrast,
many models of rational inference treat the mind as a Laplacean Demon, equipped with unlimited time,
knowledge, and computational might. Following H. Simon’s notion of satisficing, the authors have proposed
a family of algorithms based on a simple psychological mechanism: one reason decision making.
These fast and frugal algorithms violate fundamental tenets of classical rationality: They neither look up
nor integrate all information. By computer simulation, the authors held a competition between the satisficing
“Take The Best” algorithm and various “rational” inference procedures (e.g., multiple regression).
The Take The Best algorithm matched or outperformed all competitors in inferential speed and
accuracy. This result is an existence proof that cognitive mechanisms capable of successful performance
in the real world do not need to satisfy the classical norms of rational inference.

Algorithmic Mechanism Design

Algorithmic Mechanism Design

We consider algorithmic problems in a distributed setting where the
participants cannot be assumed to follow the algorithm but rather their
own self-interest. As such participants, termed agents, are capable of
manipulating the algorithm, the algorithm designer should ensure in
advance that the agents’ interests are best served by behaving correctly.
Following notions from the field of mechanism design, we suggest a
framework for studying such algorithms.
In this model the algorithmic solution is adorned with payments to
the participants and is termed a mechanism. The payments should be
carefully chosen as to motivate all participants to act as the algorithm
designer wishes. We apply the standard tools of mechanism design to
algorithmic problems and in particular to the shortest path problem.