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| public:t-713-mers:mers-23:concepts_terms [2023/08/21 15:53] – [Causation] thorisson | public:t-713-mers:mers-23:concepts_terms [2024/09/15 09:00] (current) – [INTRODUCTION] thorisson |
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| ====== INTRODUCTION ====== | ====== INTRODUCTION ====== |
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| | | EMPIRICAL REASONING | Defeasible / non-axiomatic reasoning where the data, rules, and results are restricted to the physical world. | |
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| | Main operating principle behind correlation | There is no causation without correlation | | | Main operating principle behind correlation | There is no causation without correlation | |
| | Correlation: Pitfall | Correlation does not imply causation between the variables measured! | | | Correlation: Pitfall | Correlation does not imply causation between the variables measured! | |
| | Quasi-experimental designs | Purpose: Where true experimental design is not possible, approximate it. \\ If direct control over dependent/independent variables is not possible. | | Quasi-experimental designs | Purpose: Where true experimental design is not possible, approximate it. \\ If direct control over dependent/independent variables is not possible. | |
| | How it works | 1. One-shot case study (no control group) \\ 2. Single group pre- and post-test (minimal control) \\ 3. ABAB: Single-group repeated measures (slightly less minimal control) | | | How it works | 1. One-shot case study (no control group) \\ 2. Single group pre- and post-test (minimal control) \\ 3. ABAB: Single-group repeated measures (slightly less minimal control) | |
| | Limitations | Much greater uncertainty as to the internal and external validity of the quasi-experiments than true experimental designs | | | Limitations | Much greater uncertainty as to the internal and external validity of the quasi-experiments than true experimental designs | |
| | **Phenomenon** | The world is filled with "stuff". Anything is a "thing" - even "nothing" is a thing (a concept in our minds, which is represented as neural patterns and potential for behavior). We can group any arbitrary collection of things and call it a **phenomenon**. //Example: A rock. A mountain. A planet.// (If I say that I want to study //"thingamajigs"// - something you've never heard of - I will first have to list some of the major ways in which thingamajigs can be identified. In fact, this is a good idea anyway, so as to be clear and consistent about what it is that one is studying.) | | | **Phenomenon** | The world is filled with "stuff". Anything is a "thing" - even "nothing" is a thing (a concept in our minds, which is represented as neural patterns and potential for behavior). We can group any arbitrary collection of things and call it a **phenomenon**. //Example: A rock. A mountain. A planet.// (If I say that I want to study //"thingamajigs"// - something you've never heard of - I will first have to list some of the major ways in which thingamajigs can be identified. In fact, this is a good idea anyway, so as to be clear and consistent about what it is that one is studying.) | |
| | The scientific method is independent of topic... | One can study **any phenomenon** with the scientific method, including claims of telepathy; selection of topic is independent of method -- there is nothing inherently "unscientific" about studying any subject. (Close-mindedness //is//, however, very unscientific.) \\ In other words, given that science gets us the most reliable ("best") knowledge to build on at any time, we should take it seriously. But not so seriously as to exclude the possibility that it's wrong. (Because in fact we already know that **all** scientific knowledge is wrong -- i.e. every scientific theory to date has limits to its scope that we know of.) | | | The scientific method is independent of topic... | One can study **any phenomenon** with the scientific method, including claims of telepathy; selection of topic is independent of method -- there is nothing inherently "unscientific" about studying any subject. (Close-mindedness //is//, however, very unscientific.) \\ In other words, given that science gets us the most reliable ("best") knowledge to build on at any time, we should take it seriously. But not so seriously as to exclude the possibility that it's wrong. (Because in fact we already know that **all** scientific knowledge is wrong -- i.e. every scientific theory to date has limits to its scope that we know of.) | |
| | ... yet methodology varies significantly by field |For example: \\ - Illegal to make experiments on living human brains \\ - Difficult to make comparative studies in sociology or space science. | | | How can we trust our knowledge? | The scientific method is a **General Way of Producing Trustworthy Knowledge.** It is independent of topic. Therefore, it can also be used for AI systems. (In fact, it can easily be argued that something very similar to the scientific method is happening when humans learn cumulatively -- with a few caveats that we will carefully cover in this course.) | |
| | Computer Science | Direct testing of applications and programs. \\ User-driven studies. \\ Models and simulations. \\ Logical and mathematical proofs. | | |
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| ====Models==== | ====Models==== |
| | **Model** | A model is a "cartoon" of a phenomenon -- an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question. | | | What it is | A model is a "cartoon" of a phenomenon -- an information structure that captures the most important (preferably all the important) aspects of a phenomenon in question. | |
| | All scientific theories present a model | No matter how explicit or implicit, all scientific theories are models of the world. //Best known example: E=mc^2// | | | All scientific theories present a model | No matter how explicit or implicit, all scientific theories are models of the world. //Best known example: E=mc^2// | |
| | Science vs. Mathematics | Mathematics is **axiomatic**: Some a-priori premises are (and must be) assumed. | | | Science vs. Mathematics | Mathematics is **axiomatic**: Some a-priori premises are (and must be) assumed. \\ Science is non-axiomatic: We do not know the full set of rules that govern the universe, and we will never know. | |
| | Science vs. Engineering | In science we look for the model; \\ in engineering we mold the world to behave like our model. | | | Science vs. Engineering | In science we look for the model; \\ in engineering we mold the world to behave like our model. | |
| | Science + Math | We strive to make scientific theories (models of the world) mathematical because of the //compactness, precision,// and //specificity// this can give us. However, it is not guaranteed solely through the use of math because //a model must detail how it maps to the thing it is a model of//. If this is not done properly the math provides //no benefits//. \\ Mapping a model to its reference: A good scientist does it properly; a bad scientist does it sloppily; the wannabe ignores it happily. \\ Bottom line: Being mathematical is //no guarantee// for good science - it is neither necessary nor sufficient. | | | \\ Science + Math | We strive to make scientific theories (models of the world) mathematical because of the //compactness, precision,// and //specificity// this can give us. However, it is not guaranteed solely through the use of math because //a model must detail how it maps to the thing it is a model of//. If this is not done properly the math provides //no benefits//. \\ Mapping a model to its reference: A good scientist does it properly; a bad scientist does it sloppily; the wannabe ignores it happily. \\ Bottom line: Being mathematical is //no guarantee// for good science - it is neither necessary nor sufficient. | |
| | Science + Engineering + Math: The Holy Trinity | The three fields so defined support each other: Building better scientific models helps us engineer better; engineering better helps us build new tools for doing science better. Both are bootstrapped by philosophy and clarified through math. | | | Science + Engineering + Math: The Holy Trinity | The three fields so defined support each other: Building better scientific models helps us engineer better; engineering better helps us build new tools for doing science better. Both are bootstrapped by philosophy and clarified through math. | |
| | The universe: Nothing is given | How do we know that the sun will come up tomorrow? What evidence do we have? Can we prove it mathematically that the sun will come up tomorrow? \\ The only thing we know for sure is that we can perceive things in the world and that "I am here now". (This principle is most famously captured by Rene Descartes who wrote "I think, therefore I am".) \\ But since that perception is provided/generated by the same universe that we want to claim "exists" through those senses, using those grey cells, we cannot possibly know **for sure** what that really is, and hence whether it can be trusted. \\ Therefore, the universe is (and cannot be anything but) **non-axiomatic**. | | | \\ The universe: Nothing is given | How do we know that the sun will come up tomorrow? What evidence do we have? Can we prove it mathematically that the sun will come up tomorrow? \\ The only thing we know for sure is that we can perceive things in the world and that "I am here now". (This principle is most famously captured by Rene Descartes who wrote "I think, therefore I am".) \\ But since that perception is provided/generated by the same universe that we want to claim "exists" through those senses, using those grey cells, we cannot possibly know **for sure** what that really is, and hence whether it can be trusted. \\ Therefore, the universe is (and cannot be anything but) **non-axiomatic**. | |
| | Computer Science | A creative mix of empirical science, engineering and mathematics. \\ Direct testing of applications and programs; user studies. \\ Models and simulations. \\ Logical and mathematical proofs. | | | \\ Computer Science | A creative mix of empirical science, engineering and mathematics. \\ Direct testing of applications and programs; user studies. \\ Models and simulations. \\ Logical and mathematical proofs. | |
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