Risk and Decision-Making Flashcards

In the Pymetrics 'Money Exchange' game, if you are the proposer, offering a split that feels fair to the other party (even if not equal) is strategically important because:

Answer: The responder can reject the offer, leaving both parties with nothing

In ultimatum-style exchanges, responders who feel treated unfairly often reject offers, resulting in zero for both parties.

Which of the following best describes 'framing effects' in the context of Pymetrics risk tasks?

Answer: Choosing differently based on whether outcomes are described as gains or losses, even when mathematically identical

Framing effects occur when logically equivalent options are chosen differently because one is framed as a gain and another as a loss.

A Pymetrics candidate is risk-averse in the gain domain but risk-seeking in the loss domain. This pattern is most consistent with:

Answer: Prospect theory as described by Kahneman and Tversky

Prospect theory specifically predicts risk aversion for gains and risk-seeking for losses, reflecting how people evaluate outcomes relative to a reference point.

In a Pymetrics task where you must decide how much to invest in a risky project, which strategy would likely score best for roles requiring financial risk management?

Answer: Calibrate investment size to the risk-reward profile of each round

Calibrating investment to each round's risk-reward ratio demonstrates sophisticated financial judgment and disciplined risk management.

When Pymetrics games require you to learn which of several options pays out most often, what learning style does the algorithm typically reward?

Answer: Systematic exploration followed by exploitation of the best option

Systematic exploration to identify the best option, then exploiting it, reflects adaptive reinforcement learning that Pymetrics scores favorably.

In Pymetrics games involving interpersonal trust (e.g., deciding how much money to send to a partner who may or may not return it), high senders who receive nothing back and then send high amounts again are demonstrating:

Answer: Persistent high baseline trust regardless of feedback

Continuing to send high amounts after being betrayed suggests an unconditionally high trust baseline rather than evidence-based trust calibration.

In a Pymetrics risk task that tracks your performance across 20 rounds, which learning curve would an algorithm most favorably interpret?

Answer: Improving performance after early exploration as you identify optimal strategies

An upward learning curve, especially after early exploration, signals adaptive intelligence and the ability to optimize strategy over time.