设计提升幸福感——量化幸福

古往今来对于幸福的研究，涉及哲学、心理学、社会学、文化学、医学等诸多学科领域，呈现出多视域、多维度、跨学科研究的特点。然而，在众多的研究视角中，设计在幸福的维度却被长期忽视了。究其原因，幸福通常被视为一种主观的心理感受，而设计则被视为物质的外在世界，这就让许多人看不到两者之间的内在联系。

设计的本质正是改变。设计可以作为解决问题的手段，通过设计行为改变现状，可以提升幸福感。广义层面的设计不仅包含可见的物的设计，还包含非物的工作流程的设计、情感体验设计、服务设计、社会设计、品牌故事设计等等。我们通过对幸福感的研究和分类，将幸福感拆解为12种真正的、值得提倡的幸福来源，以探讨设计如何增加幸福来源的数量，进而增加幸福的总量。

《幸福测量》一书中，作者运用顺向推演的方式对影响幸福总量的“幸福感因子”进行探索，通过建立模型方程并代入不同时期的权威统计数据进行计算，得出不同幸福感的因子对幸福感总量影响的量级程度。我们在此研究基础上进行逆向思考，对以上过程进行反推：改变幸福感因子，便能影响幸福总量即幸福感。而设计的本质是改变，本身即是改变幸福变量的重要途径之一。如此，设计可以通过对幸福变量进行干预，从而达到提升幸福总量即幸福感的目的。

若想通过设计干预幸福变量以增加幸福来源，必须对幸福进行测量。在测量与比较的基础上，才能谈对幸福感的提升。“量化幸福”旨在通过科学、系统的方法，以数字化的方式捕捉和评估个体对幸福的感受。这种方法不仅使我们能够更准确地了解幸福的构成要素，还有助于政府和社会决策者制定更有针对性的政策，以提高人们的整体生活质量。

我们提出一套可行的新幸福测量模型与公式，并将12种幸福感来源融入其中，公式呈现如下：

ln（H）=αln（a）+βln（b）+γln（c）+......+e（残差可忽略）

其中，H（happiness）作为幸福感，是因变量，a，b，c...是因子，作为影响幸福感的自变量；α，β，γ在此公式中作为系数，表示幸福感自变量对幸福感的影响；e是样本残差，是模型与实际数据之间的差值，而ln是对数函数，可理解为数学上的形式转换，方便样板数据对模型的拟合。

Design Enhancing Happiness—Quantifying Happiness

The study of happiness throughout history spans multiple disciplines, including philosophy, psychology, sociology, cultural studies, and medicine, reflecting a multi-perspective, multi-dimensional, and interdisciplinary approach. However, amidst various research angles, design has long been overlooked in the realm of happiness. The reason for this is that happiness is often seen as a subjective psychological experience, while design is viewed as a matter of the material, external world. This has led many to miss the intrinsic connection between the two.

The essence of design is change. Design can serve as a means to solve problems, and through design actions, it can change the status quo and enhance happiness. In a broader sense, design not only includes the design of tangible objects but also the design of intangible work processes, emotional experiences, services, social systems, brand storytelling, and more. By studying and categorizing happiness, we break it down into 12 genuine and commendable sources of happiness. The goal is to explore how design can increase the number of these sources, thereby increasing the total happiness.

In the book Measuring Happiness, the author explores the "happiness factors" that influence the total amount of happiness by using forward inference. Through the establishment of model equations and the substitution of authoritative statistical data from different periods, the author calculates the magnitude of the influence each happiness factor has on overall happiness. Building on this research, we reverse the thinking process: by altering the happiness factors, we can affect the total happiness, or happiness level. Since the essence of design is change, it becomes one of the key methods for changing happiness variables. Thus, design can intervene in happiness variables to enhance total happiness.

If we aim to intervene in happiness variables through design to increase sources of happiness, we must first measure happiness. Only through measurement and comparison can we talk about enhancing happiness. "Quantifying Happiness" aims to use scientific and systematic methods to capture and evaluate an individual's perception of happiness in a digital form. This method not only allows us to more accurately understand the components of happiness, but it also assists governments and social policymakers in crafting more targeted policies to improve the overall quality of life.

We propose a feasible new happiness measurement model and formula, incorporating the 12 sources of happiness. The formula is presented as follows:

ln⁡(H)=αln⁡(a)+βln⁡(b)+γln⁡(c)+⋯+e (residuals can be ignored)\ln(H) = \alpha \ln(a) + \beta \ln(b) + \gamma \ln(c) + \dots + e \, (\text{residuals can be ignored})ln(H)=αln(a)+βln(b)+γln(c)+⋯+e(residuals can be ignored)

In this formula, HHH (happiness) represents the dependent variable (happiness level), while a,b,c,…a, b, c, \dotsa,b,c,… are the independent variables (factors) that influence happiness. The coefficients α,β,γ\alpha, \beta, \gammaα,β,γ represent the impact of each factor on happiness. eee represents the residual error, which is the difference between the model and actual data, and ln⁡\lnln is the logarithmic function, serving as a mathematical transformation to facilitate fitting sample data into the model.

Prof. Chen Zhengda