You have Horizon as both a fixed effect and a random effect. That's almost always not a good idea. It should be either fixed or random, and there are a bunch of competing considerations for when to include things as random such as:

• when we don't care about the fixed effect of a factor
• when a factor has a large number of levels
• when we have sampled from larger population of levels
• when we want to generalise the inferences to other levels of the factor that were not in our sample

The reasons for treating a factor as fixed are:

• when the research question specifically concerns inference for that factor
• when the factor has a relatively few number of levels
• when we have the entire population of levels for that factor

In this case it is fairly clear that all these considerations point towards modelling Horizon as fixed:

• because the research question involves inference on Horizon
• because we have only 2 levels of Horizon
• because the 2 level of horizon are the entire population of horizons

You have Horizon as both a fixed effect and a random effect. That's almost always not a good idea. It should be either fixed or random, and there are a bunch of competing considerations for when to include things as random such as:

• when we don't care about the fixed effect of a factor
• when a factor has a large number of levels
• when we have sampled from larger population of levels
• when we want to generalise the inferences to other levels of the factor that were not in our sample

The reasons for treating a factor as fixed are:

• when the research question specifically concerns inference for that factor
• when the factor has a relatively few number of levels
• when we have the entire population of levels for that factor

In this case it is fairly clear that all these considerations point towards modelling Horizon as fixed:

• because the research question involves inference on Horizon
• because we have only 2 levels of Horizon
• because the 2 levels of horizon are the entire population of horizons