Recent results in the ML community have revealed that learning algorithms used to compute the optimal strategy for the leader to commit to in a Stackelberg game, are susceptible to manipulation by the follower. Such a learning algorithm operates by querying the best responses or the payoffs of the follower, who consequently can deceive the algorithm by responding as if their payoffs were much different than what they actually are. For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the payoffs that would make the learning algorithm compute a commitment so that best responding to it maximizes the follower's utility, according to the true payoffs. While this problem has been considered before, the related literature only focused on the simplified scenario in which the payoff space is finite, thus leaving the general version of the problem unanswered. In this paper, we fill this gap by showing that it is always possible for the follower to efficiently compute (near-)optimal payoffs for various scenarios of learning interaction between the leader and the follower.

Recent results in the ML community have revealed that learning algorithms used to compute the optimal strategy for the leader to commit to in a Stackelberg game, are susceptible to manipulation by the follower. Such a learning algorithm operates by querying the best responses or the payoffs of the follower, who consequently can deceive the algorithm by responding as if their payoffs were much different than what they actually are. For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the payoffs that would make the learning algorithm compute a commitment so that best responding to it maximizes the follower's utility, according to the true payoffs. While this problem has been considered before, the related literature only focused on the simplified scenario in which the payoff space is finite, thus leaving the general version of the problem unanswered. In this paper, we fill this gap by showing that it is always possible for the follower to efficiently compute (near-)optimal payoffs for various scenarios of learning interaction between the leader and the follower.

Executive and Leadership Coach, Lecturer, Founder of unabridged – engaging your power and potential for greater personal and social impact. As a leader, do you aim to learn something new every day, or do you stay in your comfort zone? In a 2015 Harvard Business Review article, Kenneth Mikkelsen and Harold Jarche said, "Leaders must get comfortable with living in a state of continually becoming, a perpetual beta mode. Leaders that stay on top of society's changes do so by being receptive and able to learn." However, some people have a fixed mindset, believing their IQ, personality, skills or physical state cannot be changed.

Recent results have shown that algorithms for learning the optimal commitment in a Stackelberg game are susceptible to manipulation by the follower. These learning algorithms operate by querying the best responses of the follower, who consequently can deceive the algorithm by using fake best responses, typically by responding according to fake payoffs that are different from the actual ones. For this strategic behavior to be successful, the main challenge faced by the follower is to pinpoint the fake payoffs that would make the learning algorithm output a commitment that benefits them the most. While this problem has been considered before, the related literature has only focused on a simple setting where the follower can only choose from a finite set of payoff matrices, thus leaving the general version of the problem unanswered. In this paper, we fill this gap by showing that it is always possible for the follower to efficiently compute (near-)optimal fake payoffs, for various scenarios of learning interaction between the leader and the follower. Our results also establish an interesting connection between the follower's deception and the leader's maximin utility: through deception, the follower can induce almost any (fake) Stackelberg equilibrium if and only if the leader obtains at least their maximin utility in this equilibrium.

Workplace learning and development (L&D) departments have increasingly relied upon EdTech to deliver valuable professional development to employees throughout the pandemic. Naturally, this has prompted a number of educators to confront a question that has long been looming: will these new technologies eventually displace their roles? For a variety of reasons, the answer to this question is "no", chief amongst which is the fact that humans have essential skills and qualities that are all too challenging to replicate in their robotic counterparts. From empathy and compassion, to creativity and improvisation, these virtues make skilled HR professionals and training vendors much sought-after in the workplace. That is not to say that job functions will not change as domains like artificial intelligence (AI) and robotics continue to develop.

The future of work is one of the most widely discussed topics by senior executives across all industries. A mere internet search on the subject will pull up 238,000,000 results, more than 70 business books, countless conferences, and too many podcasts and blog posts to count. I have a few out there myself. I believe this is the topic du jour because organizations are desperately trying to find the crystal ball that will enable them not only to stay ahead of their competition, but also enter into new markets and beat new competitors. I was recently asked what a leading-edge learning strategy might look like in the next five years.