I don't mean to be dismissive but it sounds like you're simply not familiar with the research in the areas you're discussing.
Also, on your latter point, doing research is not as simple as 'exploring possibilities, finding a hit, then exploring similar possibilities.' The problem is that research-space is an extremely high-dimensional, bumpy space. Simply finding new avenues of research that would produce results - null or otherwise - is often a non-trivial problem that requires a lot of intelligence and wisdom. For one thing, it would be a waste if the avenue had already been considered before. And when you find some new possibility, actually evaluating that possibility is itself a non-trivial problem that frequently requires a lot of money and time to do. And then when you get the result, it's common for the result to be only slightly significant, and come with a lot of caveats and special conditions, requiring further study just to make sure it's not a fluke.
From the outside, it can seem like science consists of a bunch of 'clueless' workers and the occasional genius who makes an astounding discovery. In reality, everyone is essentially clueless in the grand scheme of things, and most 'major discoveries' are only deemed so years after the fact.
I've been thinking lately about what is the optimal way to organize scientific research both for individuals and for groups. My first idea: research should have a long-term goal. If you don't have a long-term goal, you will end up wasting a lot of time on useless pursuits. For instance, my rough thought process of the goal of economics is that it should be “how do we maximize the productive output of society and distribute this is in an equitable manner without preventing the individual from being unproductive if they so choose?”, the goal of political science should be “how do we maximize the government's abilities to provide the resources we want while allowing individuals the freedom to pursue their goals without constraint toward other individuals?”, and the goal of psychology should be “how do we maximize the ability of individuals to make the decisions they would choose if their understanding of the problems they encounter was perfect?” These are rough, as I said, but I think they go further than the way most researchers seem to think about such problems.
Political science seems to do the worst in this area in my opinion. Very little research seems to have anything to do with what causes governments to make correct decisions, and when they do research of this type, their evaluation of correct decision making often is based on a very poor metric such as corruption. I think this is a major contributor to why governments are so awful, and yet very few political scientists seem to have well-developed theories grounded in empirical research on ways to significantly improve the government. Yes, they have ideas on how to improve government, but they're frequently not grounded in robust scientific evidence.
Another area I've been considering is search parameters of moving through research topics. An assumption I have is that the overwhelming majority of possible theories are wrong such that only a minority of areas of research will result in something other than a null outcome. Another assumption is that correct theories are generally clustered. If you get a correct result in one place, there will be a lot more correct results in a related area than for any randomly chosen theory. There seems like two major methods for searching through the landscape of possibilities. One method is to choose an area where you have strong reason to believe there might be a cluster nearby that fits with your research goals and then randomly pick isolated areas of that research area until you get to a major breakthrough, then go through the various permutations of that breakthrough until you have a complete understanding of that particular cluster area of knowledge. Another method would be to take out large chunks of research possibilities, and to just throw the book at it basically. If you come back with nothing, then you can conclude that the entire section is empty. If you get a hit, you can then isolate the many subcomponents and figure out what exactly is going on. Technically I believe the chunking approach should be slightly faster than the random approach, but only by a slight amount unless the random approach is overly isolated. If the cluster of most important ideas are at 10 to the -10th power, and you isolate variables at 10 to the -100th power, then time will be wasted going back up to the correct level. You have to guess what level of isolation will result in the most important insights.
One mistake I think is to isolate variables, and then proceed through the universe of possibilities systematically one at a time. If you get a null result in one place, it's likely true that very similar research will also result in a null result. Another mistake I often see is researchers not bothering to isolate after they get a hit. You'll sometimes see thousands of studies on the exact same thing without any application of reductionism eg the finding that people who eat breakfast are generally healthier. Clinical and business researchers seem to most frequently make this mistake of forgetting reductionism.
I'm also thinking through what types of research are most critical, but haven't gotten too far in that vein yet. It seems like long-term research (40+ years until major breakthrough) should be centered around the singularity, but what about more immediate research?