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Life is one long string of decisionmaking, even if none of them is major. This is certainly the impression we get from reading cognitive neuroscience journals. A great many studies these days seem to involve 'decisionmaking under uncertainty' (otherwise known as gambling). As a married couple, we have now clocked up just over 50 years of decisionmaking together. We still frequently avoid or delay decisions, but we know that this does not pay off in the long run. And, when we do make decisions, we usually make them jointly. In case this sounds too good to be true, we hasten to add that it's not always easy and often involves arguments - despite, or perhaps because, we are both cognitive neuroscientists ourselves. Actually, argument turns out to be a well-kept secret in group decisionmaking. But before we turn to the value of acrimony, let's look at some of the reasons why we believe that people can make better decisions together than they can on their own.

Great podcast from our own 2ser & UTS Business School! "latest episode of #ThinkBusinessFutures @2ser, with Dr Geetanjali Saluja @UTSMarketing @UTS_Business, discussing her research into moral decision making, and Adam Ferrier, author of 'The Advertising Effect: How to Change Behaviour'" Discusses some of the framing, cognitive bias, and their impact on decision making that we talked about in class

It's not just disadvantaged and vulnerable groups that struggle with financial decision-making. People who are highly educated in finance also make poor decisions - for instance, by focusing too much on growing their assets and ignoring risks. But studies show that when regulation is effective and the financial system can be trusted, even consumers with limited financial knowledge and information-processing capabilities have the potential to deal with complex financial decisions. For example, when considering mortgage protection insurance, applicants stand to benefit from knowing the actual risk of events like serious illness or injury that can affect their ability to meet monthly loan repayments.

Why probability and understanding statistical fallacies matters Some of our most important decisions are shaped by something as random as the order in which we make them. The gambler's fallacy, as it's known, affects loan officers, federal judges -- and probably you too. How to avoid it? The first step is to admit just how fallible we all are.

'It's important to me that I make my own decisions, but I often wonder how much they are actually influenced by cultural and societal norms, by advertising, the media and those around me. We all feel the need to fit in, but does this prevent us from making decisions for ourselves? In short, can I ever be a truly free thinker?' Richard, Yorkshire. While being the lone "captain of your soul" is a reassuring idea, the truth is rather more nuanced. The reality is that we are social beings driven by a profound need to fit in - and as a consequence, we are all hugely influenced by cultural norms.

Medicine's decades-long march toward patient autonomy means patients are often now asked to make the hard decisions - to weigh trade-offs, to grapple with how their values suggest one path over another. This is particularly true when medical science doesn't offer a clear answer: Doctors encourage patients to decide where evidence is weak, while making strong recommendations when evidence is robust. But should we be doing the opposite?People in general are not great at evaluating risk. They worry more about shark attacks than car crashes.

Teach people to think critically about claims and comparisons using these concepts, urge Andrew D. Oxman and an alliance of 24 researchers - they will make better decisions.

Interesting discussion of evidence on what does, and does not, work in tackling gender bias in recruitment and management processes. Evidence shows that skills-based assessment tasks (where candidates are given tests that replicate the work they'll actually do on the job) and structured interviews (where all candidates are given the same questions in the same order) have a positive impact on diverse recruitment. Unstructured interviews are more likely to allow unfair bias to creep in. Making promotion and pay processes more transparent can reduce pay inequality: when decisions are reviewed by others, managers realise they need to be objective and evidence-based. Evidence also shows women ask for less money than men. To encourage them to negotiate more, employers should make the possible salary range for roles clear. Studies indicate that women are put off negotiating when they're not sure what a reasonable offer is. "A lot of employers are genuinely really keen to reduce the gender pay gap, and also want to show they're making a change. But they're starved for information about what is likely to work,"

Algorithms are increasingly making decisions that affect ordinary people's lives. One example of this is so-called "algorithmic lending", with some companies claiming to have reduced the time it takes to approve a home loan to mere minutes. But can computers become better judges of financial risk than human bank tellers? Some computer scientists and data analysts certainly think so.

Interesting discussion of data on vote recounts and using electronic or hand counting methods (in America where they use electronic voting machines quite commonly). These numbers represent three main kinds of disputes, Foley told me. First, candidates (and their lawyers) argue over what ballots should be counted and which should be thrown out as ineligible. Then, they argue over which candidate specific ballots should count for. Finally, they argue over whether all the eligible votes were counted correctly - the actual recount. Humans are much better than machines at making decisions around the first two kinds of ambiguous disputes, Stewart said, but evidence suggests that the computers are better at counting. Michael Byrne, a psychology professor at Rice University who studies human-computer interaction, agreed. "That's kind of what they're for," he said.

For decades, researchers and physicians tended to think about pregnancies as either planned or unplanned. But new data reveals that for a significant group of women, their feelings don't neatly fit into one category or another. As many as one-fifth of women who become pregnant aren't sure whether they want a baby. This fact may reshape how doctors and policymakers think about family planning. For women who are unsure, it doesn't seem enough for physicians to counsel them on pregnancy prevention or prenatal care. "In the past we thought of it as binary, you want to be pregnant or not, so you need contraception or a prenatal vitamin," said Maria Isabel Rodriguez, an obstetrician-gynecologist at Oregon Health and Science University whose research focuses on family planning and contraceptive policy. "But it's more of a continuum." The new data comes from a recent change in the Centers for Disease Control and Prevention's big survey of new mothers, now allowing them to answer a question about their pregnancy desires by saying "I wasn't sure." It shows that some women want to avoid making a decision about becoming pregnant, or have strong but mixed feelings about it.

Any great story means visualization and detail. It takes the small additions of those details to build a picture in someone's mind to truly make the story complete. The same goes for analytics and data. Data is just a collection of numbers until you turn it into a story. Showing reports and dashboards can be overwhelming without adding a narrative to the data. Any great insight explains what happened, why it is important and how you can use it to turn it into something actionable. Data visualization is using data and statistics in creative ways to show patterns and draw conclusions about a hypothesis, or prove theories, that can help drive decisions in the organization.

The Supreme Court does not compute. Or at least some of its members would rather not. The justices, the most powerful jurists in the land, seem to have a reluctance - even an allergy - to taking math and statistics seriously. For decades, the court has struggled with quantitative evidence of all kinds in a wide variety of cases. Sometimes justices ignore this evidence. Sometimes they misinterpret it. And sometimes they cast it aside in order to hold on to more traditional legal arguments. (And, yes, sometimes they also listen to the numbers.) Yet the world itself is becoming more computationally driven, and some of those computations will need to be adjudicated before long. Some major artificial intelligence case will likely come across the court's desk in the next decade, for example. By voicing an unwillingness to engage with data-driven empiricism, justices - and thus the court - are at risk of making decisions without fully grappling with the evidence. This problem was on full display earlier this month, when the Supreme Court heard arguments in Gill v. Whitford, a case that will determine the future of partisan gerrymandering - and the contours of American democracy along with it. As my colleague Galen Druke has reported, the case hinges on math: Is there a way to measure a map's partisan bias and to create a standard for when a gerrymandered map infringes on voters' rights?

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation's series on the science of free will.

Today's Examine dives into the maths. We'll look at the best estimates on catching and being injured by COVID-19, the chances of being harmed by the AstraZeneca vaccine, and the other broader risks and benefits. Hopefully, at the end of this, you are armed to make a better decision.

Interesting look at data around private healthcare and marketisation. Each year, for well over a decade, more people have faced higher health insurance deductibles. The theory goes like this: The more of your own money that you have to spend on health care, the more careful you will be - buying only necessary care, purging waste from the system. But that theory doesn't fully mesh with reality: High deductibles aren't working as intended. A body of research - including randomized studies - shows that people do in fact cut back on care when they have to spend more for it. The problem is that they don't cut only wasteful care. They also forgo the necessary kind. This, too, is well documented, including with randomized studies. People don't know what care they need, which is why they consult doctors.