How to Make Life Decisions

Halifax, Nova Scotia; American Samoa; Queens, New York; Lansing, Michigan; Gurugram, India. I often ask patients where they’re from. Practicing in San Diego, the answers are a geography lesson. People from around the world come here. I sometimes add the more interesting question: How’d you end up here? Many took the three highways to San Diego: the Navy, the defense industry (like General Dynamics), or followed a partner. My Queens patient had a better answer: Super Bowl XXII. On Sunday, Jan. 31st, 1988, the Redskins played the Broncos in San Diego. John Elway and the Broncos lost, but it didn’t matter. “I was scrapin’ the ice off my windshield that Monday morning when I thought, that’s it. I’m done! I drove to the garage where I worked and quit on the spot. Then I drove home and packed my bags.”

In a paper on how to make life decisions, this guy would be Exhibit A: “Don’t overthink it.” That approach might not be suitable for everyone, or for every decision. It might actually be an example of how not to make life decisions (more on that later). But, is there a best way to go about making big life decisions?

The first treatise on this subject was a paper by one Franklin, Ben in 1772. Providing advice to a friend on how to make a career decision, Franklin argued: “My way is to divide half a sheet of paper by a line into two columns; writing over the one Pro and over the other Con.” This “moral algebra” as he called it was a framework to put rigor to a messy, organic problem.

wrawecihokilospeslofriphohobuchibiletutawrospepehophastephechewrubrelopadrocosleswobislimimumesoclupiuiposwocihistithophabru

The flaw in this method is that in the end you have two lists. Then what? Do the length of the lists decide? What if some factors are more important? Well, let’s add tools to help. You could use a spreadsheet and assign weights to each variable. Then sum the values and choose based on that. So if “not scraping ice off your windshield” is twice as important as “doubling your rent,” then you’ve got your answer. But what if you aren’t good at estimating how important things are? Actually, most of us are pretty awful at assigning weights to life variables – having bags of money is the consummate example. Seems important, but because of habituation, it turns out to not be sustainable. Note Exhibit B, our wealthy neighbor who owns a Lambo and G-Wagen (AMG squared, of course), who just parked a Cybertruck in his driveway. Realizing the risk of depending on peoples’ flawed judgment, companies instead use statistical modeling called bootstrap aggregating to “vote” on the weights for variables in a prediction. If you aren’t sure how important a new Rivian or walking to the beach would be, a model can answer that for you! It’s a bit disconcerting, I know. I mean, how can a model know what we’d like? Wait, isn’t that how Netflix picks stuff for you? Exactly.

Ok, so why don’t we just ask our friendly personal AI? “OK, ChatGPT, given what you know about me, where can I have it all?” Alas, here we slam into a glass wall. It seems the answer is out there but even our life-changing magical AI tools fail us. Mathematically, it is impossible to have it all. An illustrative example of this is called the economic “impossible trinity problem.” Even the most sophisticated algorithm cannot find an optional solution to some trinities such as fixed foreign exchange rate, free capital movement, and an independent monetary policy. Economists have concluded you must trade off one to have the other two. Impossible trinities are common in economics and in life. Armistead Maupin in his “Tales of the City” codifies it as Mona’s Law, the essence of which is: You cannot have the perfect job, the perfect partner, and the perfect house at the same time. (See Exhibit C, one Tom Brady).

[embed:render:related:node:267456]

This brings me to my final point, hard decisions are matters of the heart and experiencing life is the best way to understand its beautiful chaos. If making rash judgments is ill-advised and using technology cannot solve all problems (try asking your AI buddy for the square root of 2 as a fraction) what tools can we use? Maybe try reading more novels. They allow us to experience multiple lifetimes in a short time, which is what we need to learn what matters. Reading Dorothea’s choice at the end of “Middlemarch” is a nice example. Should she give up Lowick Manor and marry the penniless Ladislaw or keep it and use her wealth to help others? Seeing her struggle helps us understand how to answer questions like: Should I give up my academic practice or marry that guy or move to Texas? These cannot be reduced to arithmetic. The only way to know is to know as much of life as possible.

My last visit with my Queens patient was our last together. He’s divorced and moving from San Diego to Gallatin, Tennessee. “I’ve paid my last taxes to California, Doc. I decided that’s it, I’m done!” Perhaps he should have read “The Grapes of Wrath” before he set out for California in the first place.

Dr. Benabio is director of Healthcare Transformation and chief of dermatology at Kaiser Permanente San Diego. The opinions expressed in this column are his own and do not represent those of Kaiser Permanente. Dr. Benabio is @Dermdoc on Twitter. Write to him at dermnews@mdedge.com.

Halifax, Nova Scotia; American Samoa; Queens, New York; Lansing, Michigan; Gurugram, India. I often ask patients where they’re from. Practicing in San Diego, the answers are a geography lesson. People from around the world come here. I sometimes add the more interesting question: How’d you end up here? Many took the three highways to San Diego: the Navy, the defense industry (like General Dynamics), or followed a partner. My Queens patient had a better answer: Super Bowl XXII. On Sunday, Jan. 31st, 1988, the Redskins played the Broncos in San Diego. John Elway and the Broncos lost, but it didn’t matter. “I was scrapin’ the ice off my windshield that Monday morning when I thought, that’s it. I’m done! I drove to the garage where I worked and quit on the spot. Then I drove home and packed my bags.”

In a paper on how to make life decisions, this guy would be Exhibit A: “Don’t overthink it.” That approach might not be suitable for everyone, or for every decision. It might actually be an example of how not to make life decisions (more on that later). But, is there a best way to go about making big life decisions?

The first treatise on this subject was a paper by one Franklin, Ben in 1772. Providing advice to a friend on how to make a career decision, Franklin argued: “My way is to divide half a sheet of paper by a line into two columns; writing over the one Pro and over the other Con.” This “moral algebra” as he called it was a framework to put rigor to a messy, organic problem.

wrawecihokilospeslofriphohobuchibiletutawrospepehophastephechewrubrelopadrocosleswobislimimumesoclupiuiposwocihistithophabru

The flaw in this method is that in the end you have two lists. Then what? Do the length of the lists decide? What if some factors are more important? Well, let’s add tools to help. You could use a spreadsheet and assign weights to each variable. Then sum the values and choose based on that. So if “not scraping ice off your windshield” is twice as important as “doubling your rent,” then you’ve got your answer. But what if you aren’t good at estimating how important things are? Actually, most of us are pretty awful at assigning weights to life variables – having bags of money is the consummate example. Seems important, but because of habituation, it turns out to not be sustainable. Note Exhibit B, our wealthy neighbor who owns a Lambo and G-Wagen (AMG squared, of course), who just parked a Cybertruck in his driveway. Realizing the risk of depending on peoples’ flawed judgment, companies instead use statistical modeling called bootstrap aggregating to “vote” on the weights for variables in a prediction. If you aren’t sure how important a new Rivian or walking to the beach would be, a model can answer that for you! It’s a bit disconcerting, I know. I mean, how can a model know what we’d like? Wait, isn’t that how Netflix picks stuff for you? Exactly.

Ok, so why don’t we just ask our friendly personal AI? “OK, ChatGPT, given what you know about me, where can I have it all?” Alas, here we slam into a glass wall. It seems the answer is out there but even our life-changing magical AI tools fail us. Mathematically, it is impossible to have it all. An illustrative example of this is called the economic “impossible trinity problem.” Even the most sophisticated algorithm cannot find an optional solution to some trinities such as fixed foreign exchange rate, free capital movement, and an independent monetary policy. Economists have concluded you must trade off one to have the other two. Impossible trinities are common in economics and in life. Armistead Maupin in his “Tales of the City” codifies it as Mona’s Law, the essence of which is: You cannot have the perfect job, the perfect partner, and the perfect house at the same time. (See Exhibit C, one Tom Brady).

[embed:render:related:node:267456]

This brings me to my final point, hard decisions are matters of the heart and experiencing life is the best way to understand its beautiful chaos. If making rash judgments is ill-advised and using technology cannot solve all problems (try asking your AI buddy for the square root of 2 as a fraction) what tools can we use? Maybe try reading more novels. They allow us to experience multiple lifetimes in a short time, which is what we need to learn what matters. Reading Dorothea’s choice at the end of “Middlemarch” is a nice example. Should she give up Lowick Manor and marry the penniless Ladislaw or keep it and use her wealth to help others? Seeing her struggle helps us understand how to answer questions like: Should I give up my academic practice or marry that guy or move to Texas? These cannot be reduced to arithmetic. The only way to know is to know as much of life as possible.

My last visit with my Queens patient was our last together. He’s divorced and moving from San Diego to Gallatin, Tennessee. “I’ve paid my last taxes to California, Doc. I decided that’s it, I’m done!” Perhaps he should have read “The Grapes of Wrath” before he set out for California in the first place.

Dr. Benabio is director of Healthcare Transformation and chief of dermatology at Kaiser Permanente San Diego. The opinions expressed in this column are his own and do not represent those of Kaiser Permanente. Dr. Benabio is @Dermdoc on Twitter. Write to him at dermnews@mdedge.com.

Halifax, Nova Scotia; American Samoa; Queens, New York; Lansing, Michigan; Gurugram, India. I often ask patients where they’re from. Practicing in San Diego, the answers are a geography lesson. People from around the world come here. I sometimes add the more interesting question: How’d you end up here? Many took the three highways to San Diego: the Navy, the defense industry (like General Dynamics), or followed a partner. My Queens patient had a better answer: Super Bowl XXII. On Sunday, Jan. 31st, 1988, the Redskins played the Broncos in San Diego. John Elway and the Broncos lost, but it didn’t matter. “I was scrapin’ the ice off my windshield that Monday morning when I thought, that’s it. I’m done! I drove to the garage where I worked and quit on the spot. Then I drove home and packed my bags.”

In a paper on how to make life decisions, this guy would be Exhibit A: “Don’t overthink it.” That approach might not be suitable for everyone, or for every decision. It might actually be an example of how not to make life decisions (more on that later). But, is there a best way to go about making big life decisions?

The first treatise on this subject was a paper by one Franklin, Ben in 1772. Providing advice to a friend on how to make a career decision, Franklin argued: “My way is to divide half a sheet of paper by a line into two columns; writing over the one Pro and over the other Con.” This “moral algebra” as he called it was a framework to put rigor to a messy, organic problem.

wrawecihokilospeslofriphohobuchibiletutawrospepehophastephechewrubrelopadrocosleswobislimimumesoclupiuiposwocihistithophabru

The flaw in this method is that in the end you have two lists. Then what? Do the length of the lists decide? What if some factors are more important? Well, let’s add tools to help. You could use a spreadsheet and assign weights to each variable. Then sum the values and choose based on that. So if “not scraping ice off your windshield” is twice as important as “doubling your rent,” then you’ve got your answer. But what if you aren’t good at estimating how important things are? Actually, most of us are pretty awful at assigning weights to life variables – having bags of money is the consummate example. Seems important, but because of habituation, it turns out to not be sustainable. Note Exhibit B, our wealthy neighbor who owns a Lambo and G-Wagen (AMG squared, of course), who just parked a Cybertruck in his driveway. Realizing the risk of depending on peoples’ flawed judgment, companies instead use statistical modeling called bootstrap aggregating to “vote” on the weights for variables in a prediction. If you aren’t sure how important a new Rivian or walking to the beach would be, a model can answer that for you! It’s a bit disconcerting, I know. I mean, how can a model know what we’d like? Wait, isn’t that how Netflix picks stuff for you? Exactly.

Ok, so why don’t we just ask our friendly personal AI? “OK, ChatGPT, given what you know about me, where can I have it all?” Alas, here we slam into a glass wall. It seems the answer is out there but even our life-changing magical AI tools fail us. Mathematically, it is impossible to have it all. An illustrative example of this is called the economic “impossible trinity problem.” Even the most sophisticated algorithm cannot find an optional solution to some trinities such as fixed foreign exchange rate, free capital movement, and an independent monetary policy. Economists have concluded you must trade off one to have the other two. Impossible trinities are common in economics and in life. Armistead Maupin in his “Tales of the City” codifies it as Mona’s Law, the essence of which is: You cannot have the perfect job, the perfect partner, and the perfect house at the same time. (See Exhibit C, one Tom Brady).

[embed:render:related:node:267456]

This brings me to my final point, hard decisions are matters of the heart and experiencing life is the best way to understand its beautiful chaos. If making rash judgments is ill-advised and using technology cannot solve all problems (try asking your AI buddy for the square root of 2 as a fraction) what tools can we use? Maybe try reading more novels. They allow us to experience multiple lifetimes in a short time, which is what we need to learn what matters. Reading Dorothea’s choice at the end of “Middlemarch” is a nice example. Should she give up Lowick Manor and marry the penniless Ladislaw or keep it and use her wealth to help others? Seeing her struggle helps us understand how to answer questions like: Should I give up my academic practice or marry that guy or move to Texas? These cannot be reduced to arithmetic. The only way to know is to know as much of life as possible.

My last visit with my Queens patient was our last together. He’s divorced and moving from San Diego to Gallatin, Tennessee. “I’ve paid my last taxes to California, Doc. I decided that’s it, I’m done!” Perhaps he should have read “The Grapes of Wrath” before he set out for California in the first place.

Dr. Benabio is director of Healthcare Transformation and chief of dermatology at Kaiser Permanente San Diego. The opinions expressed in this column are his own and do not represent those of Kaiser Permanente. Dr. Benabio is @Dermdoc on Twitter. Write to him at dermnews@mdedge.com.