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)
  

Asynchronous Collaboration in Online Games

Asynchronous Collaboration in Online Games

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.

Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency

Iterative Combinatorial Auctions: Achieving Economic and Computational Efficiency

A fundamental problem in building open distributed systems is to design mechanisms that compute optimal system-wide solutions despite the self-interest of individual users and computational agents. Classic game-theoretic solutions are often prohibitively expensive computationally. For example, the Generalized Vickrey Auction (GVA) is an efficient and strategy-proof solution to the combinatorial allocation problem (CAP), in which agents demand bundles of items, but every agent must reveal its value for all possible bundles and the auctioneer must solve a sequence of NP-hard optimization problems to compute the outcome. I propose iBundle, an iterative combinatorial auction in which agents can bid for combinations of items and adjust their bids in response to bids from other agents. iBundle computes the efficient allocation in the CAP when agents follow myopic best-response bidding strategies, bidding for the bundle(s) that maximize their surplus taking the current prices as fixed. iBundle solves problems without complete information revelation from agents and terminates in competitive equilibrium. Moreover, an agent can follow a myopic best-response strategy with approximate values on bundles, for example with lower- and upper- bounds. My approach to iterative mechanism design decomposes the problem into two parts. First, I use linear programming theory to develop an efficient iterative auction under the assumption that agents will follow a myopic best-response bidding strategy. Second, I extend the approach to also compute Vickrey payments at the end of the auction. This makes myopic best-response a sequentially-rational strategy for agents in equilibrium, inheriting many of the useful game-theoretic properties of the GVA. iBundle implements a primal-dual algorithm, CombAuction, for the CAP, computing a feasible primal (the provisional allocation) and a feasible dual (the ask prices) that satisfy complementary slackness conditions. An extended auction, iBundle Extend & Adjust, interprets a primal-dual algorithm, VickAuction, as an iterative auction. VickAuction computes the efficient allocation and Vickrey payments with only best-response information from agents. Experimental results demonstrate that iBundle Extend & Adjust, which keeps iBundle open for a second phase before adjusting prices towards Vickrey payments, computes Vickrey payments across a suite of problems.

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.

Draft

Identity Mechanisms
Jurisdictions
As libraries of implied contracts.
Not necessarily defined geographically, but must define how conflicts between jurisdictions are resolved.

Strategic preferences as main influence on the outcome
Manual commands for socializing
Automated suggestions to bring preferences in sync with observed behavior

attenuated needs
the more some means to achieve satisfaction is exercised, the deeper this need becomes
this amplifies both positive and negative effects

(almost) no service that works 100% reliably - communication, transportation, search, and reports are performed by agents with their own limitations and agenda
all UI is built from interaction with agents - e.g., no automated "satisfy this purchase order" button, but you can have an assistant agent to whom you can express such a request (which may fail to be satisfied by mistake or malice)

SUBJECTIVE PERFORMANCE MEASURES IN OPTIMAL INCENTIVE CONTRACTS

SUBJECTIVE PERFORMANCE MEASURES IN OPTIMAL INCENTIVE CONTRACTS

Incentive contracts often include important subjective components that mitigate
incentive distortions caused by imperfect objective measures. This paper explores the
combined use of subjective and objective performance measures in (respectively)
implicit and explicit incentive contracts. We show that the presence of sufficiently
effective explicit contracts can render all implicit contracts infeasible, even those that
would otherwise yield the first-best. We also show, however, that in some
circumstances objective and subjective measures are complements: neither an explicit
nor an implicit contract alone yields positive profit, but an appropriate combination of
the two does. Finally, we consider subjective weights on objective measures.