Science behind Gyfted
What we’re building rests on science, data, computing and people. Combining cutting edge algorithms, psychology and economic design rooted in scientific research.
The evolution of any workplace begins with recruiting
Building blocks
We believe enabling the discovery of hidden talent and job matching at scale is a noble pursuit.
We use technology, the science of measurement and behavioral design to create the best, fastest way for anyone to get hired. In the age where talent is distributed and opportunity is becoming uncoupled from geography this is a must-do.
Psychometrics
Assessments
We’re building cutting edge, engaging assessment tools based on psychometrics to empower teams and individuals with insights into personality, preferences, motivations, skills, and behaviors. Psychometrics is the science of psychological measurement of personality, traits, abilities and attitudes.
Be yourself at work
We believe that everyone should be able understand their personality, cognitive and psycho-demographic profile in a privacy-oriented means, and in the most useful ways: for job matching and personal development. We’re democratizing psychometrics in this way for the most important use-case - jobs.
Real-time value
Given modern technology and the power of computation, with the proliferation of AI and online data and digital footprints, not only the sources of data for measurement, but the methods and speed of measurement have improved to enable real-time use-cases. This presents an enormous opportunity to improve job markets.
Short history of psychometrics
Psychometrics developed over decades of research in psychology, academia, the military and industry. The twentieth century saw evaluation methods improve and psychometrics plays an increasing role in society, business, education, politics, medicine, recruiting and organizational behavior, as well as statistics and biometrics. Recruitment has been a key area of the practical application of psychometrics.
Sir Francis Galton is often referred to as the father of psychometrics. He devised the first measures of characteristics that people possess and how those characteristics create ‘fit’ for various roles, for instance, how sensory and motor functioning (reaction time, visual acuity, physical strength) matter, creating these first domains of scientific psychology. James McKeen Cattell then pioneered the field of psychometric assessments for research and knowledge that ultimately led to the development of modern tests.
The leading book on psychometrics has been written by one of our co-founders Dr Michal Kosinski, Associate Professor at Stanford GSB, world leading psychometrician and computational psychologist.
“Modern Psychometrics: The Science of Psychological Assessment ”
by John Rust, Michal Kosinski, David Stillwell
Machine Learning
We believe ML algorithms should be used to empower job-seekers, hiring managers and recruiters to streamline and improve the way recruiting and hiring happen.
Machine learning (ML) is a sub-field of artificial intelligence (AI) and is a discipline in computer science. Machine learning enables a computer system to make predictions using historical data without being explicitly programmed, and to do so it uses a massive amount of structured and semi-structured data so that a ML model can generate accurate results or give predictions.
This way automation of tasks that can take significant time, or that are too hard for humnans to make, can be made using ML and the power of modern computing. Machine learning is related to computational statistics - making predictions using computers; mathematical optimization delivers methods; data mining - exploratory data analysis through unsupervised learning; neural networks - mimicing the work of biological brains; and predictive analytics in business settings.
ML algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. They are also used for online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, and advertising exchanges. The most important application of ML from our point of view is in content recommendation systems.
“The Hundred Page Machine Learning Book”
by Andriy Burkov
“The work in academia and education”
by prof. Andrew Ng
Market Design
Markets exist where there is supply and demand. Not all markets function well in a vacuum and it takes rules and mechanisms to improve their functioning eg. rule of law. Well-functioning markets depend on detailed rules, and this in particular applies to marketplaces where matching two or more sides of a marketplace is needed for a ‘transaction’ to occur.
Market design is largely developed through game theory and experimental economics.
Market design deals with bringing together two sides of a market efficiently. As prof. Al Roth states. Market design is a practical methodology for the creation of markets of certain properties - it is partially based on mechanism design and behavior design.
In some markets, prices may be used to induce the desired outcomes — these markets are the study of auction theory eg. radio spectrum markets. In other markets, prices may not be used — these markets are the study of matching theory eg. kidney exchanges.
Job markets are Marketplaces
They are markets based on both matching theory and pricing. Job markets as marketplaces are not in balance ie. supply and demand continuously appear and dissapear (making them harder to balance in comparison to inventory on an e-commerce platform or marketplace). To function properly, markets need to do at least three things:
Provide thickness
– proportional scale on buyer and seller side
– proportional scale on buyer and seller side
Make it safe for buyers and sellers
to reveal or act on confidential information they may hold
to reveal or act on confidential information they may hold
Overcome any congestion by enabling the conduct of transactions fast enough eg. by acting as a faster clearinghouse
Key problem with job markets
Search is expensive, information is hidden, withheld, there’s much “noise”
Prices are often missing, job salaries are not published
Matching is very costly, person-job-manager fit and recruitment “dating” pains
Companies in particular, but also job-seekers, play games and refuse to show their ‘price’ or reveal true information about them. In practice, companies are trying to ‘measure’ candidates often using ineffective tools, costly processes, or many simply cannot afford to do so - which is why most rely on CVs as biographic data and on unstructured interviews for ‘screening’, with big companies leveraging assessments as stand-along static tools. These are given what’s possible very suboptimal ways to select personnel.
We know that job market stakeholders have the following incentives:
Job boards want to sell you more ads for as long as possible
Assessment providers want to sell you volumes of tests that you might not need
Recruitment agencies are inefficient and extremely expensive.
This is all costly and leads to huge frictional and other costs and undesirable outcomes (eg. false negatives, false positives, health costs etc.). There’s market failures we can correct for.
Auction Design and Market Design
Nobel-prize winning economists prof. Alvin Roth and prof. Paul Milgrom of Stanford University are considered the fathers of auction design and market design.
Paul Milgrom states that "market design is a kind of economic engineering, utilizing laboratory research, game theory, algorithms, simulations, and more. Its challenges inspire us to rethink longstanding fundamentals of economic theory." Alvin Roth has developed cutting edge systems for matching doctors with hospitals, children with schools, and organ (kidney) donors with patients.
“Supply and demand drive both stock markets and labor markets, but someone who wants to buy or sell shares in a company goes through very different procedures from those followed by a job seeker or an employer. Moreover, labor markets work differently from one another: Doctors aren’t hired the way lawyers, professional baseball players, or new MBAs are. Market designers try to understand these differences and the rules and procedures that make various kinds of markets work well or badly. Their aim is to know the workings and requirements of particular markets well enough to fix them when they’re broken or to build markets from scratch when they’re missing.”
– prof. Alvin Roth
“Who Gets What and Why: the New Economics of Matchmaking”
by Alvin E. Roth
“The Art of Designing Markets”
by Lee Freeman-Shor
At Gyfted we focus on eliminating information bias by not collecting data on age, gender and race, debiasing data to enable fairnes, and above all providing a pseudonymous experience to improve decision-making in personnel selection. Meritocracy and objectivity help us shape UX for better outcomes.
Much of recruitment (selecting and evaluating people for work, school, sports, military and other organizations) is driven by conscisous biases and unconscious biases. It sometimes helps but it most often leads to suboptimal outcomes. This is because of various biases and heuristics. That is why we use psychometrics and assessments to objectively help measure and select candidates for roles based on a multitude of vectors and factors.
Behavioral Economics and Design
Behavioral economics studies the effects of psychological, cognitive, emotional, cultural and social factors on the decisions of individuals and institutions and how those decisions vary from those implied by classical economic theory.
This field has been significantly developed by many psychologists, and most recently by Nobel Prize-winning psychologist (who won the economics prize) prof. Daniel Kahneman for his work in behavioral economics “for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty”.
Behavioral design is a sub-category of design, which is concerned with how design can shape, or be used to influence human behavior.
Incentives shape our behaviors and actions depending on what the circumstances, context and our inner motivations, values system and abilities are. This is why this field has been hugely important in recent times in areas such as public health, policy, finance, education, and organizational behavior at large.
“Thinking Fast, and Slow”
by Daniel Kahneman
“Noise: A Flaw in Human Judgement”
by Daniel Kahneman