31:T6bd,# The philosophy of choosing between extending and improving lives This is a representation of ideas from [The elephant in the bednet: the importance of philosophy when choosing between extending and improving lives](https://www.happierlivesinstitute.org/report/the-elephant-in-the-bednet) (and relevant [discussion](https://forum.effectivealtruism.org/posts/JgqEqsa6iAtqGLYmw/the-elephant-in-the-bednet-the-importance-of-philosophy-when-1)), by Michael Plant, Joel McGuire, and Samuel Dupret. No new ideas are presented here, it is only intended to present visuals and hopefully clarify and explain the general concepts. It starts with a basic example of a possible life change (or 'intervention') that affects personal wellbeing (getting a dog) in order to introduce a visualisation of wellbeing measurement and define what is meant by a WELLBY. It then uses the same visual structure to look a life-saving interventions and shows three different ways to account for the badness of death. For two of these the definition of a 'neutral point' is important, and this is also visualised. Based on suggestions from the original authors, the effect of progress on expected wellbeing, and the holding of a weighted combination of different accounts of death are also investigated. It does not go into detail on the philosophical arguments for different views or use these views to look at charity cost-effectiveness. For these and more detail the reader is referred to the [original post](https://www.happierlivesinstitute.org/report/the-elephant-in-the-bednet). All ideas originate with the original authors, and any inaccuracies or inconsistencies are likely introduced by myself (please comment if any errors are found).32:T4d8,${viewCredenceSplit2[0].percent}% ${textcolor('Epicureanism', {background: '#9C9691', color: 'black'})} (being dead is not bad for the dead person) : no WELLBYs ${(viewCredenceSplit2[1].percent)}% ${textcolor('TRIA', '#A192D7')} (Time-relative interest account – WELLBYs lost depend on the age at which a person dies) : ${(wellbyCountTriaProgress % 1) !== 0 ? wellbyCountTriaProgress.toFixed(1) : wellbyCountTriaProgress} WELLBYs ${(viewCredenceSplit2[2].percent)}% ${textcolor('Deprivationism', '#E18F3E')} (all potential wellbeing is lost – WELLBYs lost equal to a full life) : ${(wellbyCountDepriProgress % 1) !== 0 ? wellbyCountDepriProgress.toFixed(1) : wellbyCountDepriProgress} WELLBYs ... would give (${viewCredenceSplit2[0].percent/100}×0 + ${viewCredenceSplit2[1].percent/100}×${(wellbyCountTriaProgress % 1) !== 0 ? wellbyCountTriaProgress.toFixed(1) : wellbyCountTriaProgress} + ${viewCredenceSplit2[2].percent/100}×${(wellbyCountDepriProgress % 1) !== 0 ? wellbyCountDepriProgress.toFixed(1) : wellbyCountDepriProgress} = ) **${((wellbyCountTriaProgress*viewCredenceSplit2[1].percent/100)+(wellbyCountDepriProgress*viewCredenceSplit2[2].percent/100)).toFixed(1)}** WELLBYs gained by the life-saving intervention. 33:T25b4,function wellbeingChart(data, intervention, interventionTitle, connectednessAge, neutral, { marginTop = 20, // top margin, in pixels marginRight = 30, // right margin, in pixels marginBottom = 40, // bottom margin, in pixels marginLeft = 40, // left margin, in pixels width = 940, // outer width, in pixels height = 250 // outer height, in pixels } = {}) { // Construct scales and axes. const xScale = d3.scaleLinear([0, data.baseline.length], [marginLeft, width - marginRight]); const yScale = d3.scaleLinear([0, 10], [height - marginBottom, marginTop]); const xAxis = d3.axisBottom(xScale).ticks(width / 80).tickSizeOuter(0); const xGrid = d3.axisBottom(xScale).ticks(data.baseline.length).tickSizeOuter(0); const yAxis = d3.axisLeft(yScale).ticks(10); // extend data by one const baselineData = data.baseline.concat(data.baseline[data.baseline.length-1]); const interventionData = data.intervention.concat(data.intervention[data.intervention.length-1]); // calculate +- WELLBY areas var interventionDifferencePositive = []; var interventionDifferenceNegative = []; if (data.interventionValue !== undefined) { const interventionValueData = data.interventionValue.concat(data.interventionValue[data.interventionValue.length-1]); interventionDifferencePositive = baselineData.map((d,i) => [d, Math.max(d, interventionValueData[i])]); interventionDifferenceNegative = baselineData.map((d,i) => [d, Math.min(d, interventionValueData[i])]); } else { interventionDifferencePositive = baselineData.map((d,i) => [d, Math.max(d, interventionData[i])]); interventionDifferenceNegative = baselineData.map((d,i) => [d, Math.min(d, interventionData[i])]); } const pastLine = d3.line() // line generator .curve(d3.curveStepAfter) .x((d,i) => xScale(i)) .y((d) => yScale(d)); const futureLine = d3.line() // line generator .curve(d3.curveStepAfter) .x((d,i) => xScale(i+intervention)) .y((d) => yScale(d)); const area = d3.area() // area generator .curve(d3.curveStepAfter) .y0(yScale(0)) .x((d,i) => xScale(i)) .y1((d) => yScale(d)); const interventionArea = d3.area() // area generator .curve(d3.curveStepAfter) .x((d,i) => xScale(i+intervention)) .y1((d,i) => yScale(d[1])) .y0((d,i) => yScale(d[0])); const svg = d3.create("svg") // setup area .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; height: intrinsic;"); svg.append('defs') // setup positive pattern fill .append('pattern') .attr('id', 'interventionPositivePattern') .attr('patternUnits', 'userSpaceOnUse') .attr('width', 8) .attr('height', 8) .append('path') .attr('d', 'M-1,1 l4,-4 M0,8 l8,-8 M6,10 l4,-4') .attr('stroke', "green") .attr("stroke-opacity", 0.6) .attr('stroke-width', 1.5); svg.append('defs') // setup negative pattern fill .append('pattern') .attr('id', 'interventionNegativePattern') .attr('patternUnits', 'userSpaceOnUse') .attr('width', 8) .attr('height', 8) .append('path') .attr('d', 'M-1,1 l4,-4 M0,8 l8,-8 M6,10 l4,-4') .attr('stroke', "red") .attr("stroke-opacity", 0.6) .attr('stroke-width', 1.5); svg.append("path") // draw wellbeing area .attr("fill", "lightgrey") .attr("stroke", "none") .attr("d", area(baselineData)); svg.append("path") // draw intervention positive wellbeing area // .attr('fill', 'url(#interventionPositivePattern)') .attr('fill', 'green') .attr("fill-opacity", 0.25) .attr("stroke", "none") .attr("d", interventionArea(interventionDifferencePositive.slice(intervention, interventionData.length+1))); svg.append("path") // draw intervention negative wellbeing area // .attr('fill', 'url(#interventionNegativePattern)') .attr('fill', 'red') .attr("fill-opacity", 0.25) .attr("stroke", "none") .attr("d", interventionArea(interventionDifferenceNegative.slice(intervention, interventionData.length+1))); svg.append("g") // draw x-axis .attr("transform", `translate(0,${height - marginBottom})`) .call(xAxis) .call(g => g.selectAll(".domain").remove()) .call(g => g.append("text") .attr("x", width - marginRight) .attr("y", 30) //height-10) .attr("fill", "currentColor") .attr("text-anchor", "end") .text("Age →")); svg.append("g") // draw x-grid .attr("transform", `translate(0,${height - marginBottom})`) .call(xGrid) .call(g => g.selectAll(".domain").remove()) .call(g => g.selectAll(".tick text").remove()) .call(g => g.selectAll(".tick line") .attr("y2", -height + marginTop + marginBottom) .attr("stroke", "white") .attr("stroke-opacity", 0.6)); svg.append("g") // draw y-axis and grid .attr("transform", `translate(${marginLeft},0)`) .call(yAxis) .call(g => g.selectAll(".domain").remove()) .call(g => g.selectAll(".tick line").clone() .attr("x2", width - marginLeft - marginRight) .attr("stroke", "white") .attr("stroke-opacity", 0.6)) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("↑ Wellbeing")); if (connectednessAge !== undefined) { svg.append("line") // draw connectedness line .attr("stroke", "gray") .attr("stroke-width", 1) .attr("x1", xScale(0)) .attr("y1", yScale(0)) .attr("x2", xScale(connectednessAge)) .attr("y2", yScale(10)) .attr("stroke-dasharray", "7,3"); svg.append("line") // draw connectedness line .attr("stroke", "gray") .attr("stroke-width", 1) .attr("x1", xScale(connectednessAge)) .attr("y1", yScale(0)) .attr("x2", xScale(connectednessAge)) .attr("y2", yScale(10)) .attr("stroke-dasharray", "7,3"); svg.append("circle") // draw point at intervention .attr("cx", xScale(connectednessAge>intervention ? intervention : connectednessAge)) .attr("cy", yScale(connectedness*10)) .attr("r", "3") .attr("fill", "gray"); svg.append("text") // add connectedness label .attr("x", xScale(connectednessAge)-Math.min(55,xScale(connectednessAge)-55)) .attr("y", yScale(10)-10) // .attr("text-anchor", "middle") .attr("font-size", "10") .attr("font-family", "sans-serif") .text("Age of connectedness"); svg.append("text") // add connectedness percentage label .attr("x", xScale(connectednessAge>intervention ? intervention : connectednessAge) + 7) .attr("y", yScale(connectedness*10)) .attr("alignment-baseline", "hanging") .attr("font-size", "10") .attr("font-family", "sans-serif") .text((connectedness*100).toFixed(0) + "% connectedness"); } if (neutral !== undefined) { svg.append("line") // draw neutral point line .attr("stroke", "gray") .attr("stroke-width", 1) .attr("x1", xScale(0)) .attr("y1", yScale(neutral)) .attr("x2", xScale(lifeSpan)) .attr("y2", yScale(neutral)) .attr("stroke-dasharray", "7,3"); svg.append("text") // add neutral point label .attr("x", xScale(lifeSpan)) .attr("y", yScale(neutral)-5) .attr("text-anchor", "end") .attr("font-size", "10") .attr("font-family", "sans-serif") .text("Neutral point"); } svg.append("line") // draw intervention line .attr("stroke", "gray") .attr("stroke-width", 2) .attr("x1", xScale(intervention)) .attr("y1", yScale(0)) .attr("x2", xScale(intervention)) .attr("y2", yScale(9.5)) .attr("stroke-dasharray", "1,3"); svg.append("path") // draw past wellbeing line .attr("fill", "none") .attr("stroke", "black") .attr("stroke-width", 3) .attr("d", pastLine(baselineData.slice(0, intervention+1))); svg.append("path") // draw future wellbeing line .attr("fill", "none") .attr("stroke", "gray") .attr("stroke-width", 3) .attr("d", futureLine(baselineData.slice(intervention, baselineData.length+1))); // .attr("stroke-dasharray", "15,5"); svg.append("path") // draw future intervention wellbeing line .attr("fill", "none") .attr("stroke", "green") .attr("stroke-width", 3) .attr("d", futureLine(interventionData.slice(intervention, interventionData.length+1))) .attr("stroke-dasharray", "15,5"); svg.append("rect") // add intervention label background .attr("height", 15) .attr("width", 115) .attr("rx", 3) .attr("ry", 3) .attr("x", xScale(intervention)-Math.min(55,xScale(intervention)-45)-3) .attr("y", yScale(9.5)-16) .attr("fill", "white") .attr("fill-opacity", 0.75); svg.append("text") // add intervention label .attr("x", xScale(intervention)-Math.min(55,xScale(intervention)-45)) .attr("y", yScale(9.5)-5) // .attr("text-anchor", "middle") .attr("font-size", "10") .attr("font-family", "sans-serif") .text(interventionTitle); return svg.node(); }34:T1152,function badnessOfDeath(data, intervention, connectedness, { marginTop = 35, // top margin, in pixels marginRight = 30, // right margin, in pixels marginBottom = 40, // bottom margin, in pixels marginLeft = 40, // left margin, in pixels width = 940, // outer width, in pixels height = 250 // outer height, in pixels } = {}) { // Construct scales and axes. const xScale = d3.scaleLinear([0, lifeSpan], [marginLeft, width - marginRight]); const yScale = d3.scaleLinear([0, lifeSpan], [height - marginBottom, marginTop]); const xAxis = d3.axisBottom(xScale).ticks(width / 80).tickSizeOuter(0); const xGrid = d3.axisBottom(xScale).ticks(data.length).tickSizeOuter(0); const yAxis = d3.axisLeft(yScale).ticks(10); // extend data by one const baselineData = data; const line = d3.line() // line generator .curve(d3.curveLinear) .x((d,i) => xScale(i)) .y((d) => yScale(d)); const svg = d3.create("svg") // setup area .attr("width", width) .attr("height", height) .attr("viewBox", [0, 0, width, height]) .attr("style", "max-width: 100%; height: auto; height: intrinsic;"); svg.append("g") // draw x-axis .attr("transform", `translate(0,${height - marginBottom})`) .call(xAxis) .call(g => g.selectAll(".domain") .attr("stroke", "black")) .call(g => g.append("text") .attr("x", width - marginRight) .attr("y", 30) //height-10) .attr("fill", "currentColor") .attr("text-anchor", "end") .text("Age →")); svg.append("g") // draw y-axis and grid .attr("transform", `translate(${marginLeft},0)`) .call(yAxis) // .call(g => g.selectAll(".domain").remove()) .call(g => g.append("text") .attr("x", -marginLeft) .attr("y", 10) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("↑ Badness of death /")) .call(g => g.append("text") .attr("x", -marginLeft+10) .attr("y", 23) .attr("fill", "currentColor") .attr("text-anchor", "start") .text("years of life lost")); svg.append("line") // draw intervention line .attr("stroke", "gray") .attr("stroke-width", 2) .attr("x1", xScale(intervention)) .attr("y1", yScale(0)) .attr("x2", xScale(intervention)) .attr("y2", yScale(baselineData[intervention])) .attr("stroke-dasharray", "1,3"); svg.append("line") // draw connectedness line .attr("stroke", "gray") .attr("stroke-width", 1) .attr("x1", xScale(connectedness)) .attr("y1", yScale(0)) .attr("x2", xScale(connectedness)) .attr("y2", yScale(lifeSpan-2)) .attr("stroke-dasharray", "7,3"); svg.append("text") // add connectedness label .attr("x", xScale(connectedness)-Math.min(55,xScale(connectedness)-45)) .attr("y", yScale(lifeSpan-2)-5) // .attr("text-anchor", "middle") .attr("font-size", "10") .attr("font-family", "sans-serif") .text("Age of connectedness"); svg.append("path") // draw line .attr("fill", "none") .attr("stroke", "black") .attr("stroke-width", 3) .attr("d", line(baselineData)); svg.append("circle") // draw point at intervention .attr("cx", xScale(intervention)) .attr("cy", yScale(baselineData[intervention])) .attr("r", "5"); svg.append("rect") // add intervention label background .attr("height", 15) .attr("width", 92) .attr("rx", 3) .attr("ry", 3) .attr("x", (intervention>connectedness||7>intervention) ? xScale(intervention)-3 : xScale(intervention)-90-Math.min(0,xScale(intervention)-0)) .attr("y", yScale(baselineData[intervention])-20) .attr("fill", "white") .attr("fill-opacity", 0.75); svg.append("text") // add intervention label .attr("x", xScale(intervention)-Math.min(0,xScale(intervention)-0)) .attr("y", yScale(baselineData[intervention])-10) .attr("text-anchor", (intervention>connectedness||7>intervention) ? "start" : "end") .attr("font-size", "10") .attr("font-family", "sans-serif") .text(baselineData[intervention].toFixed(0) + " years of life lost"); return svg.node(); }35:[] 2f:{"title":"The philosophy of choosing between extending and improving lives","cells":[{"id":4,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":461,"value":"## Visualising wellbeing\nConsider a 25 year-old who had a good childhood, where they would rate their own wellbeing as ${tex`\\frac{8}{10}`}. Perhaps they then had some rocky teenage years that they would rate as ${tex`\\frac{6}{10}`}, but after this they levelled off at a decent enough life, where they would say their wellbeing was ${tex`\\frac{7}{10}`}. We can graph their subjective rating of wellbeing to-date (**―**), along with the expectation that this level of wellbeing will continue into the future in the absence of any major life changes (**―**). The area underneath this line (▉) is then defined as the number of wellbeing-adjusted life years (WELLBYs) gained to a certain age.","pinned":false,"hidden":false,"mode":"md"},{"id":15,"value":"wellbeingExampleChart = wellbeingChart(({\n baseline: Array(13).fill(8).concat(Array(5).fill(6)).concat(Array(lifeSpan-5-13).fill(7)),\n intervention: []\n}), 25, \" Current age\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":493,"value":"Suppose that this person is now considering the possibility of getting a pet dog. They like dogs and expect that it will increase their personal wellbeing by one point (to ${tex`\\frac{8}{10})`} for its ten year life (**– –**); however, having a dog will prevent them from taking a year off aged 30 to travel abroad, which they would otherwise have rated as ${tex`\\frac{10}{10}`} for wellbeing.\n\nLooking at the WELLBYs provided by the intervention of getting a dog, we can see that (all else being equal) the dog would provide 9 positive WELLBYs (▉) and 2 negative (▉); overall giving an increase over the baseline of **7** WELLBYs, and therefore being a good choice for this person.","pinned":false,"hidden":false,"mode":"md"},{"id":494,"value":"interventionExampleChart = wellbeingChart(({\n baseline: Array(13).fill(8)\n .concat(Array(5).fill(6))\n .concat(Array(12).fill(7)\n .concat(Array(1).fill(10))\n .concat(Array(Math.max(lifeSpan,35)-1-12-5-13).fill(7))),\n intervention: Array(13).fill(8)\n .concat(Array(5).fill(6))\n .concat(Array(7).fill(7)\n .concat(Array(10).fill(8))\n .concat(Array(Math.max(lifeSpan,35)-10-7-5-13).fill(7))),\n}), 25, \" Get dog?\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":607,"value":"## Accounting for the badness of death\nAdding up the change in WELLBYs for most life-*changing* interventions is straightforward. However, in order to compare WELLBYs for life-*saving* interventions we are forced to consider how many WELLBYs are generated by remaining alive, which depends on the philosophical approach we take. Three possible views we could take are Epicureanism, Deprivationism, or a Time-relative interest account.\n\n### Epicureanism\nThis takes the view that being dead is not bad for the dead person, as they have no subjective experience of being dead.\n\nIf we consider a life-saving intervention for a ${interventionAge} year old person, it might be able to give them continued existence to a life expectancy of ${lifeSpan} years, but under an Epicurean view we wouldn't count this as providing any WELLBYs, as they are no better off than if they were dead.","pinned":false,"hidden":false,"mode":"md"},{"id":613,"value":"epicureanismChart = wellbeingChart(({\n baseline: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill(0)),\n intervention: Array(lifeSpan).fill(exampleWellbeingLevel),\n interventionValue: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill(0)),\n}), interventionAge, \"Life-saving intervention?\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":629,"value":"### Deprivationism\nThis views the badness of death as the difference between the wellbeing you actually had compared to how much you would have had if you’d lived longer. So giving ${lifeSpan-interventionAge} years extra life to the same person with an expected wellbeing of ${tex`\\frac{${exampleWellbeingLevel}}{10}`} would provide **${(lifeSpan-interventionAge)*exampleWellbeingLevel}** WELLBYs.","pinned":false,"hidden":false,"mode":"md"},{"id":641,"value":"deprivationismChart = wellbeingChart(({\n baseline: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill(0)),\n intervention: Array(lifeSpan).fill(exampleWellbeingLevel),\n interventionValue: Array(lifeSpan).fill(exampleWellbeingLevel),\n}), interventionAge, \"Life-saving intervention?\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":649,"value":"### Time-relative interest account (TRIA)","pinned":false,"hidden":false,"mode":"md"},{"id":1002,"value":"This view considers that the badness of death depends on the age at which a person dies. Under this view the WELLBYs gained from a life-saving intervention increase from zero at birth (similar to an Epicurean view) through to the full value of expected wellbeing at some set age (similar to a Deprivationist view). This age is defined as the age at which a person is considered fully 'psychologically connected' to their later self.","pinned":false,"hidden":false,"mode":"md"},{"id":1015,"value":"For example, if the age of full psychological connectedness is ${connectednessAge}, and our life-saving intervention happens at age ${interventionAge}, then the WELLBYs gained are ${(connectedness*100).toFixed(0)}% of the total possible. If our expected level of wellbeing is ${tex`\\frac{${exampleWellbeingLevel}}{10}`} then the WELLBYs claimed from the intervention would be ${(exampleWellbeingLevel*connectedness).toFixed(1)} per year, or **${(connectedness*(lifeSpan-interventionAge)*exampleWellbeingLevel).toFixed(1)}** WELLBYs over their expected lifespan.\n\nThe effect of connectedness age, versus the age at which the life is saved, may be investigated below (note that these affect plots throughout the post).","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"viewof interventionAge = Inputs.range([0, lifeSpan], {value: 10, step: 1, label: \"Life-saving intervention age\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":698,"value":"viewof connectednessAge = Inputs.range([0, lifeSpan], {value: 20, step: 1, label: \"Connectedness age\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":684,"value":"triaChart = wellbeingChart(({\n baseline: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill(0)),\n intervention: Array(lifeSpan).fill(exampleWellbeingLevel),\n interventionValue: Array(lifeSpan).fill(exampleWellbeingLevel * connectedness),\n}), interventionAge, \"Life-saving intervention?\", connectednessAge)","pinned":false,"hidden":false,"mode":"ojs"},{"id":881,"value":"Another way to think about TRIA is to consider the years of life lost due to death at a certain age. TRIA is equivalent to a Deprivationist view if the age of connectedness is zero, in which case the years of life lost by a death immediately after birth are the full expected lifespan of the person, linearly decreasing for death at older ages. With a higher age of connectedness a death in the years up to connectedness discounts the years of life lost. ","pinned":false,"hidden":false,"mode":"md"},{"id":750,"value":"triaBadnessChart = badnessOfDeath (Array(lifeSpan+1).fill().map((_, i) => (lifeSpan - i)*Math.min(1,1-(connectednessAge-i)/(connectednessAge+0.00001))),\n interventionAge, connectednessAge)","pinned":false,"hidden":false,"mode":"ojs"},{"id":737,"value":"## The neutral point\nFor the Deprivationist and TRIA approach we have assumed above that death equates to a wellbeing level of zero, however it can be argued that low levels of wellbeing don't represent lives worth living, and therefore the 'neutral point' is some value above this. We can define this neutral point as the level at which continued existence would be overall neither good nor bad for the person. WELLBYs are therefore only gained relative to this level and its definition has a significant impact on the expected benefits of any given intervention.\n\nThe effect of the neutral point on number of WELLBYs, alongside intervention age, connectedness age (conditional on TRIA being true), expected lifespan and expected wellbeing level can be investigated below (affecting plots throughout the post).","pinned":false,"hidden":false,"mode":"md"},{"id":890,"value":"viewof neutralPoint = Inputs.range([0, 10], {value: 3, step: 1, label: \"Neutral point\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":922,"value":"Inputs.bind(Inputs.range([0, lifeSpan], {value: 10, step: 1, label: \"Life-saving intervention age\"}), viewof interventionAge)","pinned":false,"hidden":false,"mode":"ojs"},{"id":916,"value":"Inputs.bind(Inputs.range([0, lifeSpan], {value: 20, step: 1, label: \"Connectedness age\"}), viewof connectednessAge)","pinned":false,"hidden":false,"mode":"ojs"},{"id":934,"value":"viewof lifeSpan = Inputs.range([0, 100], {value: 70, step: 1, label: \"Expected lifespan\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1050,"value":"viewof exampleWellbeingLevel = Inputs.range([0, 10], {value: 6, step: 1, label: \"Previous/expected wellbeing level\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":909,"value":"neutralPointChart = wellbeingChart(({\n baseline: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill(neutralPoint)),\n intervention: Array(lifeSpan).fill(exampleWellbeingLevel),\n interventionValue: Array(lifeSpan).fill(neutralPoint + (exampleWellbeingLevel-neutralPoint) * connectedness),\n}), interventionAge, \"Life-saving intervention?\", connectednessAge, neutralPoint)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1082,"value":"Under the conditions above, **${(wellbyCountFixed % 1) !== 0 ? wellbyCountFixed.toFixed(1) : wellbyCountFixed}** WELLBYs are gained by the life-saving intervention (the green area ▉, minus the red area ▉). ","pinned":false,"hidden":false,"mode":"md"},{"id":1147,"value":"## The effect of progress\n\nThe above analysis assumes an expected wellbeing level that is fixed for the remainder of a person's life. In practice, our expectation may be that wellbeing will either increase or decrease over a persons lifetime, based on things like ailing personal health or improved standards of living in their area. We can take into account an expected increase or decrease in expected wellbeing, per decade, from the point of our intervention; this adds a gradient to our expectation line.","pinned":false,"hidden":false,"mode":"md"},{"id":1149,"value":"viewof wellbeingLevelTrend = Inputs.range([-3, 3], {value: 0.5, step: 0.1, label: \"Expected wellbeing change per decade\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1160,"value":"progressChart = wellbeingChart(({\n baseline: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill(neutralPoint)),\n intervention: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill().map((_, i) => Math.max(0,Math.min(10,exampleWellbeingLevel + (i/10)*wellbeingLevelTrend)))),\n interventionValue: Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill().map((_, i) => neutralPoint + (Math.max(0,Math.min(10,(exampleWellbeingLevel + (i/10)*wellbeingLevelTrend)))-neutralPoint) * connectedness)),\n}), interventionAge, \"Life-saving intervention?\", connectednessAge, neutralPoint)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1205,"value":"With expected wellbeing changing, the estimate is now **${(wellbyCountTriaProgress % 1) !== 0 ? wellbyCountTriaProgress.toFixed(1) : wellbyCountTriaProgress}** WELLBYs gained by the life-saving intervention. ","pinned":false,"hidden":false,"mode":"md"},{"id":1211,"value":"## Holding a combination of views\n\nAbove we looked three different philosophical viewpoints on the wellbeing gained from a life-saving intervention: Epicureanism, Deprivationism, and a Time-relative interest account (TRIA). For both the Deprivationist and TRIA viewpoints it is necessary to define a neutral point (the wellbeing level at which continued existence would be neither good nor bad) and an expected wellbeing level, that could be increasing or decreasing through the person's lifetime.\n\nPhilosophers disagree on what viewpoint is 'correct', and so one option for calculating WELLBYs is to take some form of average across all views, weighted your own personal credence in each being right. With all the values stated above (neutral point, connectedness age, wellbeing change over time, etc), the effect of changing the weighting on each view can be explored below. \n\nThe weighted combination of...","pinned":false,"hidden":false,"mode":"md"},{"id":1395,"value":"viewof viewCredenceSplit2 = new ComponentSlider({\n data: [\n {\n percent: 20,\n color: \"#9C9691\"\n },\n {\n percent: 60,\n color: \"#A192D7\"\n },\n {\n percent: 20,\n color: \"#E18F3E\",\n last: true\n }\n]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1314,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":603,"value":"#### Functions/Variables \n\n###### (no need to read past this point)","pinned":false,"hidden":false,"mode":"md"},{"id":1507,"value":"connectedness = Math.min(1,interventionAge/connectednessAge)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1087,"value":"wellbyCountFixed = { \n var baseline = Array(interventionAge).fill(exampleWellbeingLevel)\n .concat(Array(lifeSpan-interventionAge).fill(neutralPoint));\n var interventionValue = Array(interventionAge).fill(exampleWellbeingLevel)\n .concat(Array(lifeSpan-interventionAge).fill(neutralPoint + (exampleWellbeingLevel-neutralPoint) * connectedness));\n return baseline.reduce((a, b, i) => a + (Math.max(b, interventionValue[i]) - b) + (Math.min(b, interventionValue[i]) - b), 0);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1196,"value":"wellbyCountTriaProgress = { \n var baseline = Array(interventionAge).fill(exampleWellbeingLevel)\n .concat(Array(lifeSpan-interventionAge).fill(neutralPoint));\n var interventionValue = Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill().map((_, i) => neutralPoint + (Math.max(0,Math.min(10,(exampleWellbeingLevel + (i/10)*wellbeingLevelTrend)))-neutralPoint) * connectedness));\n return baseline.reduce((a, b, i) => a + (Math.max(b, interventionValue[i]) - b) + (Math.min(b, interventionValue[i]) - b), 0);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1359,"value":"wellbyCountDepriProgress = { \n var baseline = Array(interventionAge).fill(exampleWellbeingLevel)\n .concat(Array(lifeSpan-interventionAge).fill(neutralPoint));\n var interventionValue = Array(interventionAge).fill(exampleWellbeingLevel).concat(Array(lifeSpan-interventionAge).fill().map((_, i) => neutralPoint + (Math.max(0,Math.min(10,(exampleWellbeingLevel + (i/10)*wellbeingLevelTrend)))-neutralPoint)));\n return baseline.reduce((a, b, i) => a + (Math.max(b, interventionValue[i]) - b) + (Math.min(b, interventionValue[i]) - b), 0);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":748,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1477,"value":"function set(input, value) {\n input.value = value;\n input.dispatchEvent(new Event(\"input\", {bubbles: true}));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1416,"value":"import {textcolor} from \"@observablehq/text-color-annotations-in-markdown\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1392,"value":"import {ComponentSlider} from '@johnwelford/component-slider-input'","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}