The AgeGuess database, an open online resource on chronological and perceived ages of people aged 5-100.

In many developed countries, human life expectancy has doubled over the last 180 years. Underlying this higher life expectancy is a change in how we age. Biomarkers of ageing are used to quantify changes in the aging process and to determine biological age. Perceived age is such a biomarker that correlates with biological age. Here we present a unique database rich with possibilities to study the human ageing process. Using perceived age enables us to collect large amounts of data on biological age through a citizen science project, where people upload facial pictures and guess the ages of other people at www.ageguess.org . The data on perceived age we present here span birth cohorts from the years 1877 to 2012. The database currently contains around 220,000 perceived age guesses. Almost 4500 citizen scientists from over 120 countries of origin have uploaded ~4700 facial photographs. Beyond studying the ageing process, the data present a wealth of possibilities to study how humans guess ages and who is better at guessing ages.

The curious anomaly of skewed judgment distributions and systematic error in the wisdom of crowds.

Judgment distributions are often skewed and we know little about why. This paper explains the phenomenon of skewed judgment distributions by introducing the augmented quincunx (AQ) model of sequential and probabilistic cue categorization by neurons of judges. In the process of developing inferences about true values, when neurons categorize cues better than chance, and when the particular true value is extreme compared to what is typical and anchored upon, then populations of judges form skewed judgment distributions with high probability. Moreover, the collective error made by these people can be inferred from how skewed their judgment distributions are, and in what direction they tilt. This implies not just that judgment distributions are shaped by cues, but that judgment distributions are cues themselves for the wisdom of crowds. The AQ model also predicts that judgment variance correlates positively with collective error, thereby challenging what is commonly believed about how diversity and collective intelligence relate. Data from 3053 judgment surveys about US macroeconomic variables obtained from the Federal Reserve Bank of Philadelphia and the Wall Street Journal provide strong support, and implications are discussed with reference to three central ideas on collective intelligence, these being Galton's conjecture on the distribution of judgments, Muth's rational expectations hypothesis, and Page's diversity prediction theorem.